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feat: complete OCR-v2 readiness gates + blind audit validation

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- Gate 1: completeness signals for page text coverage
- Gate 2: DW figure ownership generalization with strict regression
- Gate 3: reference boundary enforcement from structure
- Gate 4: layout coverage taxonomy in audit/coverage_ledger.json
- diff_audit.py: canonical role alias mapping + auto-normalize truth_role
- ocr_structural_gate.py: table caption text-evidence fallback
- ocr-canonical-roles.md: canonical role list for audit workflow
- ocr-truth-audit.md: pre-flight checklist enforces canonical roles
- Blind audit: 5 unseen papers across domains, all PASS with coverage
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---
name: paperforge-development
description: Use when doing PaperForge engineering or internal OCR truth-audit work. Triggered by: "OCR审计" "审OCR" "审计OCR" "审一下" "审计这篇" "block review" "ocr truth audit" "annotated pages" "看下角色对不对" "块角色" "visual audit" "对照一下图片" "校对block".
---
# PaperForge Development
Developer-only skill family for internal PaperForge OCR audit work.
Use this when the task is to inspect OCR truth, classify failures, and write audit evidence without changing the runtime PaperForge skill graph.
## Route
- OCR truth audit: `workflows/ocr-truth-audit.md`
## Scripts
- Audit helper: `scripts/ocr_truth_audit.py`
## Invariant
- Truth first.
- Do not start by authoring expectations.
- Establish block-level truth from page visuals and artifacts before comparing pipeline behavior.

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# OCR Audit Report Schema
Truth-first invariant:
- Do not start by authoring expectations.
- Determine truth from page visuals and artifacts first.
- Use reports to record truth and mismatches, not to normalize incorrect output.
External vault invariant:
- The OCR source root is outside the repo.
- The audit helper must not assume a repo-local literature vault.
- The caller must provide `--source-root` or set `PAPERFORGE_OCR_ROOT`.
## Required Output Files
Write outputs under `audit/<paper_key>/`.
Concrete example:
- `audit/CAQNW9Q2/audit_report.md`
- `audit/CAQNW9Q2/audit_report.json`
- `audit/CAQNW9Q2/audit_scope.json`
- `audit/CAQNW9Q2/block_review.jsonl`
- `audit/CAQNW9Q2/coverage_check.json`
- `audit/CAQNW9Q2/block_coverage_summary.json`
- `audit/CAQNW9Q2/page_risk_summary.json`
- `audit/CAQNW9Q2/reference_intrusion_candidates.json`
- `audit/CAQNW9Q2/figure_table_ownership_summary.json`
- `audit/CAQNW9Q2/fulltext_block_mapping_summary.json`
- `audit/CAQNW9Q2/reference_span_audit.json`
- `audit/CAQNW9Q2/annotated_pages/page_*_overview.png` — all-block overview with short numeric labels
- `audit/CAQNW9Q2/annotated_pages/page_*_index.json` — per-page block-to-metadata mapping
## Primary Categories
- `frontmatter_error`
- `body_flow_error`
- `reference_span_error`
- `same_page_boundary_error`
- `backmatter_error`
- `object_ownership_error`
- `reading_order_error`
- `render_mapping_error`
## Meta Category
- `audit_truth_gap`
Use `audit_truth_gap` when the audit process fails to surface real block-level truth or when prior expectations drift away from visual truth.
## Severities
- `critical`
- `major`
- `minor`
- `cosmetic`
Severity tracks trust and downstream damage, not implementation effort.
## audit_report.json Example
```json
{
"paper_key": "CAQNW9Q2",
"mode": "high-risk",
"artifact_fingerprint": {
"result_json_hash": "...",
"structured_blocks_hash": "...",
"fulltext_hash": "...",
"document_structure_hash": "..."
},
"reviewed_pages": [1, 7, 8],
"reviewed_blocks": ["p1:b3", "p7:b12"],
"findings": [
{
"category": "reference_span_error",
"severity": "critical",
"block_ids": ["p7:b12"],
"truth": "block belongs outside reference span",
"pipeline_behavior": "rendered inside accepted references",
"root_cause_hypothesis": "same-page boundary inference overreach",
"evidence": {
"annotated_page": "annotated_pages/page_007.png",
"artifact": "structure/document_structure.json"
}
}
]
}
```
## reference_span_audit.json Example
```json
{
"reference_span": {
"status": "ACCEPT",
"span_id": "refspan_001",
"start": {
"page": 7,
"column": 1,
"y": 1032,
"block_id": "p7:b12"
},
"end": {
"page": 10,
"column": 2,
"y": 1398,
"block_id": "p10:b48"
},
"ordered_block_ids": ["p7:b12", "p7:b13"],
"inside_block_ids": ["p7:b12", "p7:b13"],
"explicitly_outside_nearby_block_ids": ["p7:b10", "p7:b11"],
"intrusion_candidates": []
}
}
```
## Minimum Reporting Expectations
- `audit_report.md` summarizes reviewed scope, freshness status, findings, and repair vs residual recommendations.
- `audit_report.json` is the machine-readable source of findings.
- `audit_scope.json` defines which pages and blocks must be reviewed for the chosen mode.
- `block_review.jsonl` is the agent-written visual truth review log.
- `coverage_check.json` is written by `verify_review_coverage.py` and must fail when mandatory reviews are missing.
- `block_coverage_summary.json` tracks which blocks were reviewed and their review state.
- `page_risk_summary.json` records page scores and reasons.
- `reference_intrusion_candidates.json` records candidate reference contamination.
- `figure_table_ownership_summary.json` records caption/asset/object mismatches.
- `fulltext_block_mapping_summary.json` records source-to-render placement checks.
- `reference_span_audit.json` records accepted reference span boundaries and intrusion checks.

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# OCR Canonical Roles
`truth_role` in `block_review.jsonl` MUST be one of these canonical pipeline roles.
Do not invent new role names — use only the set below. Non-canonical names
will be normalized by `diff_audit.py` but relying on that is a workflow violation.
## Frontmatter
| Role | When |
|------|------|
| `paper_title` | Document title on page 1 |
| `authors` | Author byline |
| `affiliation` | Author affiliation block |
| `frontmatter_support` | Correspondence, DOI, dates, data availability |
| `frontmatter_noise` | Page-1 non-structural furniture (journal name, article type, publisher strip) |
| `abstract_heading` | "Abstract" label |
| `abstract_body` | Abstract body text |
| `keywords` | Keywords block |
## Body
| Role | When |
|------|------|
| `section_heading` | Top-level section heading |
| `subsection_heading` | Second-level section heading |
| `sub_subsection_heading` | Third-level section heading |
| `body_paragraph` | Narrative body paragraph |
| `tail_candidate_body` | Body paragraph in the tail spread |
## Reference
| Role | When |
|------|------|
| `reference_heading` | "References" heading |
| `reference_item` | Individual reference entry |
## Backmatter
| Role | When |
|------|------|
| `backmatter_heading` | Backmatter section heading (Acknowledgments, Funding, etc.) |
| `backmatter_heading_candidate` | Possible backmatter heading, not yet confirmed |
| `backmatter_body` | Backmatter body paragraph |
| `backmatter_boundary_heading` | Confirmed backmatter boundary heading |
| `backmatter_boundary_candidate` | Possible backmatter boundary |
## Figure / Table
| Role | When |
|------|------|
| `figure_asset` | Figure image/media |
| `figure_caption` | Confirmed figure caption |
| `figure_caption_candidate` | Probable figure caption, not fully confirmed |
| `figure_inner_text` | Text inside a figure (panel labels, axis labels) |
| `table_caption` | Confirmed table caption |
| `table_caption_candidate` | Probable table caption, not fully confirmed |
| `table_asset` | Table image |
| `table_html` | HTML native table |
| `table_html_candidate` | Probable HTML native table |
| `media_asset` | Generic media image (when figure/table type is unclear) |
## Structural Inserts
| Role | When |
|------|------|
| `structured_insert` | Structured block (callout box, highlight, algorithm) |
| `structured_insert_candidate` | Probable structured insert |
| `non_body_insert` | Non-body decorative insert (publisher strip, sidebar filler) |
## Noise & Fallback
| Role | When |
|------|------|
| `noise` | Decorative line, page furniture, garbage OCR |
| `footnote` | Page footnote |
| `unknown_structural` | Block exists but no role fits (structural gate fallback) |
| `ocr_raw_error` | OCR extraction failure (empty text) |
| `page_header` | Running header repeat |
| `page_footer` | Running footer repeat |
## Ground Truth Only (seed roles, never final)
These are assigned as `seed_role` by the early role assignment but never appear
as the final pipeline output role. When auditing, use the final role above
even if the seed role seems more intuitive:
- `figure_title` (raw OCR label, not a final role)

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# Scripts
- `ocr_truth_audit.py` stages OCR audit artifacts and writes deterministic helper summaries under `audit/<paper_key>/`.
- It requires an explicit external OCR root via `--source-root` or `PAPERFORGE_OCR_ROOT`.
- It is developer-only and separate from `scripts/dev/` so the repo-local `paperforge-development` skill can evolve without touching the PaperForge product workflow.

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.opencode/skills/paperforge-development/scripts/diff_audit.py
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@ -19,6 +19,21 @@ from pathlib import Path
sys.path.insert(0, str(Path(__file__).resolve().parents[5]))
from paperforge.worker.ocr_artifacts import artifact_paths_for_root
# Canonical role map: alias → pipeline output role name.
_CANONICAL_ROLE = {
"media_asset": "figure_asset",
"structural_noise": "noise",
"author_list": "authors",
"running_header": "noise",
"page_marker": "noise",
"frontmatter_metadata": "frontmatter_noise",
"separator": "noise",
}
def _canonical(role: str) -> str:
return _CANONICAL_ROLE.get(role, role)
def _load_jsonl(path: Path) -> list[dict]:
if not path.exists():
@ -83,7 +98,7 @@ def main() -> None:
pipe_zone = current.get("zone", "")
pipe_text_len = len(str(current.get("text", "")).strip())
role_match = pipe_role == truth_role
role_match = _canonical(pipe_role) == _canonical(truth_role)
zone_match = not truth_zone or pipe_zone == truth_zone
if role_match:
@ -116,6 +131,14 @@ def main() -> None:
}
changed_path.write_text(json.dumps(summary, indent=2, ensure_ascii=False) + "\n", encoding="utf-8")
# Normalize truth_role to canonical form in place, so future diffs
# match even without the alias map.
for review in reviews:
raw = str(review.get("truth_role") or "")
canonical = _canonical(raw)
if canonical != raw:
review["truth_role"] = canonical
# Write back updated block_review.jsonl
_write_jsonl(review_path, reviews)

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from __future__ import annotations
import argparse
import json
from pathlib import Path
REQUIRED_FIELDS = [
"block_id",
"page",
"review_status",
"truth_role",
"truth_zone",
"truth_reference_membership",
]
def build_parser() -> argparse.ArgumentParser:
parser = argparse.ArgumentParser(description="Verify OCR truth-review coverage against audit_scope.json")
parser.add_argument("audit_dir", help="Path like audit/CAQNW9Q2")
return parser
def _load_json(path: Path) -> dict:
return json.loads(path.read_text(encoding="utf-8"))
def _load_jsonl(path: Path) -> list[dict]:
rows: list[dict] = []
if not path.exists():
return rows
for line in path.read_text(encoding="utf-8").splitlines():
line = line.strip()
if not line:
continue
rows.append(json.loads(line))
return rows
def _write_json(path: Path, payload: dict) -> None:
path.write_text(json.dumps(payload, indent=2, ensure_ascii=False), encoding="utf-8")
def verify(audit_dir: Path) -> tuple[int, dict]:
scope_path = audit_dir / "audit_scope.json"
review_path = audit_dir / "block_review.jsonl"
if not scope_path.exists():
raise SystemExit(f"Missing audit scope: {scope_path}")
if not review_path.exists():
raise SystemExit(f"Missing block review file: {review_path}")
scope = _load_json(scope_path)
reviews = _load_jsonl(review_path)
required = {str(item): True for item in scope.get("required_block_ids", [])}
reviewed_valid: dict[str, dict] = {}
invalid_rows: list[dict] = []
page_counts: dict[int, int] = {}
for row in reviews:
block_id = str(row.get("block_id", "")).strip()
if not block_id:
invalid_rows.append({"block_id": None, "reason": "missing_block_id", "row": row})
continue
missing_fields = [field for field in REQUIRED_FIELDS if row.get(field) in (None, "")]
if row.get("review_status") != "reviewed":
missing_fields.append("review_status=reviewed")
evidence = row.get("evidence") or {}
if not isinstance(evidence, dict) or not evidence.get("annotated_page"):
missing_fields.append("evidence.annotated_page")
if missing_fields:
invalid_rows.append({"block_id": block_id, "reason": "missing_fields", "fields": missing_fields})
continue
reviewed_valid[block_id] = row
page = int(row.get("page", 0) or 0)
if page > 0:
page_counts[page] = page_counts.get(page, 0) + 1
missing_block_ids = sorted([block_id for block_id in required if block_id not in reviewed_valid])
missing_pages = []
for page_row in scope.get("selected_page_requirements", []):
page = int(page_row.get("page", 0) or 0)
if page_row.get("must_review_page") and page_counts.get(page, 0) == 0:
missing_pages.append(page)
status = "PASS"
if invalid_rows or missing_block_ids or missing_pages:
status = "FAIL"
coverage_ratio = (len(required) - len(missing_block_ids)) / len(required) if required else 1.0
report = {
"paper_key": audit_dir.name,
"mode": scope.get("mode"),
"required_block_ids": sorted(required.keys()),
"reviewed_block_ids": sorted(reviewed_valid.keys()),
"missing_block_ids": missing_block_ids,
"missing_pages": missing_pages,
"invalid_rows": invalid_rows,
"coverage_ratio": coverage_ratio,
"status": status,
}
return (0 if status == "PASS" else 1), report
def main(argv: list[str] | None = None) -> int:
parser = build_parser()
args = parser.parse_args(argv)
audit_dir = Path(args.audit_dir)
if not audit_dir.exists():
raise SystemExit(f"Audit dir not found: {audit_dir}")
code, report = verify(audit_dir)
_write_json(audit_dir / "coverage_check.json", report)
print(json.dumps(report, indent=2, ensure_ascii=False))
return code
if __name__ == "__main__":
raise SystemExit(main())

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# OCR Truth Audit
Inputs:
- explicit `paper_key`, or current paper from active context
- `mode: strict | high-risk`
## Pre-flight Checklist
- [ ] Confirm this is developer/internal OCR audit work.
- [ ] Resolve one target paper.
- [ ] Confirm mode: `strict` or `high-risk`.
- [ ] Do not start by writing expectations or fixtures.
- [ ] Use visual and artifact truth first.
- [ ] Know the report contract in `../atoms/ocr-audit-report-schema.md`.
- [ ] Know the page ranking contract in `../atoms/ocr-page-risk-scoring.md`.
- [ ] Know the canonical role list in `../atoms/ocr-canonical-roles.md` — `truth_role` MUST be from that list.
## Workflow
1. Resolve the paper.
Accept an explicit `paper_key`; otherwise use the current paper in context.
Also resolve the OCR root explicitly. The literature/OCR vault is outside the repo. Pass it with `--source-root` or set `PAPERFORGE_OCR_ROOT`.
2. Refresh or check required artifacts.
Run `../scripts/ocr_truth_audit.py` with `--refresh-artifacts` when you need a fresh rebuild. Confirm availability of `block_trace`, `annotated_pages`, structured block artifacts, rendered `fulltext`, document-structure artifacts, and relevant figure/table artifacts.
3. Verify artifact freshness before auditing.
Check that the paper PDF and derived artifacts belong to the same rebuild generation. At minimum compare fingerprints for `result_json`, raw/structured blocks, document structure, figure/table inventories, reader object outputs, and `fulltext`.
4. Stop on stale artifacts.
If the required artifacts are out of sync, stop with `AUDIT_BLOCKED: stale artifacts` and record which artifacts mismatch.
5. Generate helper summaries.
Materialize helper outputs under `audit/<paper_key>/` via `../scripts/ocr_truth_audit.py`, but treat them as accelerators only, not truth.
6. Inspect truth from evidence.
Start with the overview pages (`annotated_pages/page_*.png`). Each page shows every block as a numbered rectangle, color-coded by the **pipeline's final rendered role** (after gate + normalize, the same role used in `fulltext.md`). Each of the 28 roles has a distinct color, grouped by semantic family:
- dark blue family: `paper_title` `authors` `affiliation` `frontmatter_support` `frontmatter_noise`
- purple family: `abstract_heading` `abstract_body`
- orange/red family: `section_heading` `subsection_heading` `sub_subsection_heading`
- green: `body_paragraph`
- red family: `reference_heading` `reference_item`
- purple-brown family: `backmatter_heading` `backmatter_body` `backmatter_boundary_candidate`
- amber/gold family: `media_asset` `figure_caption` `figure_caption_candidate` `figure_inner_text` `table_caption` `table_caption_candidate`
- teal family: `structured_insert` `structured_insert_candidate` `non_body_insert`
- gray family: `noise` `footnote` `unknown_structural`
The color tells you what the pipeline decided. Your job is to judge whether that decision matches visual truth.
The companion index file (`annotated_pages/page_*_index.json`) maps each label number to its full `block_id`, `role`, `zone`, `category`, `text_preview`, and `bbox`. Use the index to look up what any numbered block currently is, then judge its truth visually from the overview page.
Cross-reference against `block_trace`, document-structure artifacts, and rendered `fulltext` only after forming an initial visual judgment.
7. Write audit outputs.
Produce the required reports and summaries using `../scripts/ocr_truth_audit.py` plus the schema in `../atoms/ocr-audit-report-schema.md`.
8. Perform visual block review.
After prep outputs are written, inspect the selected pages and write `audit/<paper_key>/block_review.jsonl`. Every reviewed block must be grounded in page visuals plus bbox/artifact evidence.
**`truth_role` MUST be one of the canonical roles in `../atoms/ocr-canonical-roles.md`.**
Do not invent role names. The list includes `paper_title`, `authors`, `abstract_body`, `section_heading`, `body_paragraph`, `reference_item`, `backmatter_body`, `figure_caption`, `table_caption`, `noise`, and all other final pipeline roles.
Using old names (e.g. `media_asset` for `figure_asset`, `author_list` for `authors`, `structural_noise` for `noise`) is a workflow violation — `diff_audit.py` normalizes them, but the audit should be correct from the start.
Each line must include `block_id`, `page`, `review_status`, `truth_role`, `truth_zone`, `truth_reference_membership`, and `evidence` with `annotated_page` and `method`. Example:
```json
{"block_id":"p5:9","page":5,"review_status":"reviewed","truth_role":"body_paragraph","truth_zone":"body_zone","truth_reference_membership":"outside","evidence":{"annotated_page":"annotated_pages/page_005.png","method":"visual+bbox"},"short_reason":"Conclusion body before references, not backmatter."}
```
Use the page index to find a block's current role/zone before judging. Color category helps orient: red blocks are reference candidates, purple are backmatter candidates, etc.
9. Verify review coverage.
Run `../scripts/verify_review_coverage.py` against the paper audit directory. If required blocks are missing for the chosen mode, the audit is incomplete.
## Script Entry Point
Use:
```bash
python .opencode/skills/paperforge-development/scripts/ocr_truth_audit.py CAQNW9Q2 --source-root D:/YOUR/VAULT/System/PaperForge/ocr --mode high-risk --refresh-artifacts
```
For strict mode:
```bash
python .opencode/skills/paperforge-development/scripts/ocr_truth_audit.py DWQQK2YB --source-root D:/YOUR/VAULT/System/PaperForge/ocr --mode strict --refresh-artifacts
```
Coverage verification:
```bash
python .opencode/skills/paperforge-development/scripts/verify_review_coverage.py audit/DWQQK2YB
```
10. Classify findings.
Classify every real error into the frozen taxonomy. Use `audit_truth_gap` only when the audit layer itself misses or distorts block-level truth.
11. Recommend disposition.
For each finding, recommend `repair` when it reflects a pipeline defect worth fixing now, or `residual` when it is real but intentionally deferred or outside the current close-out boundary.
## Strict Mode
Strict mode is full-coverage and block-oriented.
- Every block gets a review state.
- Every reviewed block records truth for role, zone, order, object membership, and reference membership.
- Check role correctness against page context.
- Check zone correctness against actual placement.
- Check reading order and fulltext insertion point.
- Check figure/table ownership where applicable.
- Check reference membership and same-page boundary behavior where applicable.
- Do not infer truth from current rendered output as the first step.
Minimum review questions per block:
- What is the true role?
- What is the true zone?
- What blocks should come before and after it?
- Does it belong to a figure or table object?
- Is it inside or outside the accepted reference span?
## High-Risk Mode
High-risk mode is rapid audit for trust-sensitive layout classes.
- Rank pages with the additive score in `../atoms/ocr-page-risk-scoring.md`.
- Prioritize frontmatter pages.
- Prioritize first-reference and mixed reference pages.
- Prioritize same-page body/reference/tail mixes.
- Prioritize post-reference backmatter pages.
- Prioritize figure-dense and table-dense pages.
- Prioritize figure/table ownership and caption-matching failures.
Recommended focus targets:
- `frontmatter`
- `reference_span`
- `same_page_boundary`
- `backmatter`
- `object_ownership`
- `reading_order`
## Truth Rules
Always follow this order:
1. Determine block-level truth from page visuals and artifacts.
2. Record the truth.
3. Compare pipeline behavior against that truth.
Never reverse the order by rewriting expected truth to fit current OCR output.

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@ -1105,3 +1105,99 @@ Updated `project/current/ocr-v2-remaining-issues-2026-06-18.md` with formal Gate
- **Gate 2 DW Fig 3:** `test_dwqqk2yb_figure3_is_fully_owned_not_merely_captured` is xfail — needs group-first scoring refinement for mixed-layout pages
- DWQQK2YB Figures 2/3/4 ownership gaps (pre-existing, unchanged)
- 6 pre-existing figure test failures (pre-existing, unchanged)
### 10.9 Task 7 — Readiness Review Hole Closure (2026-06-18)
**Problem:** Post-implementation review found that Gate 1 completeness helpers were only unit-tested seams, not runtime health inputs; missing-PDF baseline semantics were misleading; and two audited papers had no readiness-class tags.
**Root cause:** The readiness pass added helper functions and taxonomy scaffolding, but did not finish the production-health wiring or the full audit-ledger classification pass.
**Fix:** wired completeness summaries into `build_ocr_health()` so the runtime health path now emits page coverage, region-completeness summary, and rendered-gap counts when PDF-side hints are present; normalized rendered-gap comparison for whitespace/case drift; changed missing-PDF coverage ratio to `None` instead of fake `1.0`; added a stronger audit contract requiring every audited paper to carry at least one layout tag; tagged `K7R8PEKW` and `SAN9AYVR` in `audit/coverage_ledger.json`; strengthened the Gate 3 boundary test with explicit zone assertions.
**Result:** The two review-critical holes are closed at the code/test level, and Gate 4 no longer leaves 25% of the audit corpus unclassified. Remaining readiness work still centers on Gate 2 figure ownership refinement.
**Test status:**
```
python -m pytest tests/test_ocr_health.py -v
→ 26 passed
python -m pytest tests/test_ocr_real_paper_audit_contracts.py -v
→ 3 passed
python -m pytest tests/test_ocr_document.py -k "reference_boundary or same_page_reference_boundary_is_resolved_upstream" -v
→ 1 passed
python -m pytest tests/test_ocr_real_paper_regressions.py -k "DWQQK2YB or CAQNW9Q2" -v
→ 5 passed, 12 skipped, 1 xfailed
```
**Next topic:** finish Gate 2 by refining group-first figure scoring for DW Figure 3 and then rerun the readiness verification sweep.
### 10.10 Task 8 — Gate 2 DW Figure 3 Ownership Closure (2026-06-18)
**Problem:** Gate 2 still failed the strict DW Figure 3 regression because the caption lived on page 40 while its owned media lived on page 39, and the inventory path only tolerated that case as ambiguous/unresolved.
**Root cause:** `build_figure_inventory()` had no usable previous-page sequential fallback for a strong next-page caption, and when a later fallback did claim the asset, the stale ambiguous entry for the same legend was left behind.
**Fix:** added a focused figure unit test for previous-page sequential fallback, taught the sequential fallback to claim the nearest unclaimed asset from the immediately previous page when the numbered caption starts the next page, and removed stale `ambiguous_figures` entries when that fallback successfully matches the legend. Then promoted the DW strict regression from `xfail` to a normal passing test.
**Result:** DW Figure 3 is now strictly matched instead of "captured but ambiguous," closing the remaining Gate 2 readiness gap on the tracked hard case.
**Test status:**
```
python -m pytest tests/test_ocr_figures.py -k "previous_page_asset_for_next_page_caption or partition_assets_by_caption_bands" -v
→ 2 passed
python -m pytest tests/test_ocr_real_paper_regressions.py -k "dwqqk2yb_figure3_is_fully_owned_not_merely_captured or DWQQK2YB" -v
→ 6 passed, 4 skipped
```
**Next topic:** rerun the broader readiness verification sweep and update the queue/docs from "Gate 2 partial" to the new all-current-gates-green state if the wider suites stay stable.
### 10.11 Task 9 — Broader Readiness Sweep After Gate 2 Closure (2026-06-18)
**Problem:** The targeted Gate 2 DW Figure 3 fix needed to be tested against the broader readiness sweep before the branch could claim all current gates were green.
**Root cause:** The Gate 2 patch introduced a broader previous-page sequential fallback rule. While it fixed the tracked DW regression, that rule also widened generic matching behavior beyond the older figure-suite assumptions.
**Fix:** Ran the broader readiness sweep across health, document, figure, real-paper regression, audit-contract, CLI/unit, and lint surfaces. Wrote the result back into project truth files instead of prematurely promoting Gate 2 to DONE.
**Result:** Gates 1, 3, and 4 remained green on the verified suites. Targeted DW Gate 2 regression initially turned green, but the first broader figure sweep exposed 7 matcher regressions, proving the new fallback rule was too broad.
**Test status:**
```
python -m pytest tests/test_ocr_health.py -v
→ 26 passed
python -m pytest tests/test_ocr_document.py -v
→ 131 passed
python -m pytest tests/test_ocr_figures.py -v
→ 82 passed, 7 failed
python -m pytest tests/test_ocr_real_paper_regressions.py -v
→ 6 passed, 46 skipped
python -m pytest tests/test_ocr_real_paper_audit_contracts.py -v
→ 3 passed
python -m pytest tests/cli/ tests/unit/ -v --tb=short
→ 283 passed
ruff check paperforge/ tests/
→ 260 findings, pre-existing repo-wide lint debt remains
```
**Next topic:** tighten the previous-page sequential fallback in `build_figure_inventory()` so it stays narrow enough to preserve the older generic figure tests while keeping DW Figure 3 matched.
### 10.12 Task 10 — Gate 2 Fallback Tightening With Layout Cross-Check (2026-06-18)
**Problem:** The first Gate 2 closure patch solved DW Figure 3 but made the generic figure matcher too permissive: sidecar fallback overreached, inline mentions were treated as formal legends, and same-page weak candidates were being forced through the sequential path.
**Root cause:** The new previous-page fallback lacked enough local layout cross-checks, and several existing generic matcher branches were still missing disambiguation guards (unnumbered sidecar legends, above-vs-below candidate ties, inline mention rejection, and caption-before-assets vs caption-after-assets partitioning).
**Fix:** tightened `build_figure_inventory()` in four places: (1) previous-page sequential fallback now requires a strong numbered next-page caption plus post-reference and page-edge geometry evidence, (2) same-page weak sequential fallback is no longer allowed to manufacture matches, (3) sidecar partition now uses a local layout-orientation cross-check to choose between caption-before-assets and caption-after-assets mapping, and (4) inline figure mentions plus close above/below asset ties are explicitly rejected/held instead of forced into matches.
**Result:** DW Figure 3 stays strictly matched, and the generic figure suite is green again. This closes Gate 2 on the currently tracked readiness suites instead of just on the one DW hard case.
**Test status:**
```
python -m pytest tests/test_ocr_figures.py -v
→ 89 passed
python -m pytest tests/test_ocr_real_paper_regressions.py -k "DWQQK2YB or CAQNW9Q2" -v
→ 6 passed, 12 skipped
python -m pytest tests/test_ocr_health.py tests/test_ocr_document.py tests/test_ocr_figures.py tests/test_ocr_real_paper_audit_contracts.py -q
→ 249 passed
```
**Next topic:** decide whether to run one last broader readiness sweep boundary before Gate 5 or move directly to preparing the bounded unseen-paper blind audit sample.

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# Group-First Figure Inventory Implementation Plan
> **For agentic workers:** REQUIRED SUB-SKILL: Use superpowers:subagent-driven-development (recommended) or superpowers:executing-plans to implement this plan task-by-task. Steps use checkbox (`- [ ]`) syntax for tracking.
**Goal:** Refactor strict figure inventory so media assets are normalized into deterministic candidate groups before legend matching. The first implementation does not attempt journal-specific layout recovery; it only changes the matching unit from individual assets to candidate groups while preserving current single-asset behavior as the default-compatible path.
**Architecture:** Keep the existing formal-legend gate, reader-figure synthesis, and rendering layers intact. Refactor `build_figure_inventory()` so it builds deterministic candidate figure groups (`single`, `same_row_pair`, `same_row_triple`) before matching legends, then matches formal legends against those groups instead of individual assets. Restrict all fallback logic so it only runs after group-first matching fails, prevent sequence-promotion from conflicting with claimed groups, and make grouped outcomes explicit in inventory metadata without changing the external reader/render schema.
**Tech Stack:** Python 3.14, pytest, PaperForge OCR workers, fixture-backed regression tests, live-vault OCR audit contracts.
---
## File Map
- Modify: `paperforge/worker/ocr_figures.py`
- Owns strict figure inventory construction.
- Current problem seam: legend loop scores individual assets before any generic grouping is applied.
- Planned changes: add candidate group builder, group scoring, group consumption, guarded fallback behavior.
- Modify: `paperforge/worker/ocr_health.py`
- Owns health metrics derived from figure inventory.
- Planned changes: report grouped-match counts and grouped-vs-single inventory composition for audit visibility.
- Modify: `tests/test_ocr_figures.py`
- Owns unit and contract behavior for `build_figure_inventory()`.
- Planned changes: add red-green coverage for group-first matching, no-regression safety for single-panel papers, and fallback constraints.
- Modify: `tests/test_ocr_figure_reader.py`
- Owns strict-inventory to reader-payload normalization tests.
- Planned changes: verify grouped strict matches materialize into a single reader figure with multi-asset visual group.
- Reference only: `tests/test_ocr_real_paper_regressions.py`
- Owns fixture-backed production-path regression gates.
- Planned use in this phase: verify no deterministic gold regressions after the inventory refactor.
- Modify: `tests/test_ocr_real_paper_audit_contracts.py`
- Owns fixture quality contract, not runtime behavior.
- Planned changes: only if needed to reflect new stronger grouped-ownership coverage requirements.
- Reference only: `tests/fixtures/ocr_real_papers/6FGDBFQN/expectations.json`
- Gold AJR expectations.
- Do not strengthen these expectations in this phase; use real-paper runs as observational shadow-checks only.
- Reference only: `paperforge/worker/ocr_figure_reader.py`
- Consumes `matched_assets` and already supports multi-asset `visual_groups`.
- Do not redesign unless grouped strict inventory exposes an incompatibility.
- Reference only: `paperforge/worker/ocr_render.py`
- Renders reader figures and figure embeds.
- Do not redesign unless grouped reader figures render incorrectly.
## Non-Negotiable Constraints
- Do not introduce journal-specific, AJR-specific, profile, or template logic in phase 1.
- Do not use image semantics, OCR panel text, or vision inference as required evidence.
- Do not override strong existing exact single-panel matches unless the new grouped match is strictly higher-confidence under deterministic rules.
- Do not let sequential fallback consume assets that belong to a valid grouped candidate.
- Do not let `_promote_sequence_matches()` create matches that conflict with already claimed grouped assets.
- Do not change reader/render public payload shape unless a test proves it is necessary.
- Do not support cross-page grouping in phase 1.
- Do not support arbitrary grids in phase 1; only `single`, `same_row_pair`, `same_row_triple`.
## Risks To Avoid
1. **Breaking ordinary single-image papers**
- Current majority path works because a caption often truly maps to one image.
- Guardrail: `single_asset` remains a valid candidate group and must compete in the same scoring system.
2. **Over-grouping unrelated nearby media**
- Adjacent images, headshots, icon strips, or author bios can be geometrically close.
- Guardrail: group builder must filter to figure-like media only and enforce same-page, same-row, size-similarity, and spacing thresholds.
3. **Double consumption across figures**
- Existing code consumes asset indices early.
- Guardrail: switch consumption unit from asset index to group asset-set ownership; once a grouped match wins, all member assets are reserved together.
4. **Sequential fallback undoing correct grouping**
- Current fallback greedily assigns one caption to one remaining asset.
- Guardrail: fallback only sees truly unclaimed assets after grouped matching has finalized ownership.
5. **Reader/render incompatibility**
- Downstream code assumes `matched_assets` list exists and reader figures build `visual_groups` from it.
- Guardrail: preserve `matched_assets` shape; add metadata fields instead of replacing existing ones.
6. **False confidence on truncated legends**
- Weak captions should still HOLD/AMBIGUOUS rather than force-group.
- Guardrail: keep `_is_insufficient_legend_evidence()` gate in front of grouped matching.
## Implementation Strategy
### Phase 1: Promote Generic Grouping Into Primary Inventory Path
Refactor the strict inventory path so candidate groups are built before legend matching. Reuse existing `_media_clusters()` concepts where possible, but tighten them into deterministic group candidates instead of late unresolved-only clusters.
Candidate group model should include:
```python
{
"group_id": "group_001",
"page": 4,
"group_type": "same_row_pair", # single_asset | same_row_pair | same_row_triple
"asset_block_ids": [7, 8],
"media_blocks": [...],
"cluster_bbox": [x1, y1, x2, y2],
"row_band": {"y_center": 546.0, "height_mean": 336.5},
"group_confidence": 0.82,
"group_evidence": ["same_page", "same_row_band", "size_similar", "tight_horizontal_gap"],
}
```
Legend matching should then evaluate candidate groups, not raw assets, with `single_asset` represented as a group of size 1.
### Phase 2: Keep Current Fallbacks, But De-power Them
`sequential_fallback` and `unresolved_clusters` stay, but only after group-first exact matching has run. They should never outrank deterministic grouped exact matches.
### Phase 3: Shadow-Check Real Papers Without Tightening Gold Expectations
After the generic path is stable, run observational real-paper compare checks on known multi-panel papers. Do not tighten AJR or other gold ownership expectations in this phase.
## Task 1: Add Red Test For Group-First Matching In AJR-Like Layout
**Files:**
- Modify: `tests/test_ocr_figures.py`
- Test: `tests/test_ocr_figures.py`
- [ ] **Step 1: Write the failing test**
Add a focused unit test near the existing figure matching tests:
```python
def test_group_first_matching_prefers_same_row_pair_over_single_asset() -> None:
from paperforge.worker.ocr_figures import build_figure_inventory
blocks = [
{
"block_id": 1,
"role": "figure_caption",
"text": "Fig. 2 A and B, MRI and gross anatomic correlation.",
"page": 3,
"bbox": [80, 120, 420, 210],
"marker_signature": {"type": "figure_number", "number": 2},
"zone": "display_zone",
"style_family": "legend_like",
},
{"block_id": 2, "role": "media_asset", "raw_label": "image", "page": 3, "bbox": [450, 120, 780, 520]},
{"block_id": 3, "role": "media_asset", "raw_label": "image", "page": 3, "bbox": [805, 120, 1130, 520]},
]
inventory = build_figure_inventory(blocks, page_width=1200)
matched = inventory["matched_figures"]
assert len(matched) == 1
assert matched[0]["figure_number"] == 2
assert [a["block_id"] for a in matched[0]["matched_assets"]] == [2, 3]
```
- [ ] **Step 2: Run test to verify it fails**
Run: `python -m pytest tests/test_ocr_figures.py -k group_first_matching_prefers_same_row_pair_over_single_asset -v`
Expected: FAIL because current inventory will match only one asset or produce ambiguous/no-asset behavior.
- [ ] **Step 3: Commit the failing test only**
```bash
git add tests/test_ocr_figures.py
git commit -m "test: add failing pair-group figure inventory case"
```
## Task 2: Add Candidate Figure Group Builder In `ocr_figures.py`
**Files:**
- Modify: `paperforge/worker/ocr_figures.py`
- Test: `tests/test_ocr_figures.py`
- [ ] **Step 1: Add helper skeletons for group-first inventory**
Insert helpers near `_media_clusters()`:
```python
def _bbox_width(bbox: list[float]) -> float:
return float(bbox[2] - bbox[0])
def _bbox_height(bbox: list[float]) -> float:
return float(bbox[3] - bbox[1])
def _bbox_center_y(bbox: list[float]) -> float:
return float(bbox[1] + bbox[3]) / 2.0
def _candidate_group_entry(group_id: str, page: int, media_blocks: list[dict], group_type: str, evidence: list[str]) -> dict:
return {
"group_id": group_id,
"page": page,
"group_type": group_type,
"asset_block_ids": [b.get("block_id") for b in media_blocks if b.get("block_id") is not None],
"media_blocks": media_blocks,
"cluster_bbox": _cluster_bbox([b.get("bbox", [0, 0, 0, 0]) for b in media_blocks]),
"group_evidence": evidence,
}
```
- [ ] **Step 2: Add deterministic same-row grouping helper**
Add a helper that derives `single_asset`, `same_row_pair`, and `same_row_triple` candidates from same-page media:
```python
def _build_candidate_figure_groups_from_assets(assets: list[dict], page_width: float = 1200) -> list[dict]:
media = [
b for b in assets
if not b.get("_non_body_media")
and (b.get("role") == "figure_asset" or (b.get("role") == "media_asset" and b.get("raw_label", "") in {"image", "chart", "figure"}))
]
media.sort(key=lambda b: (b.get("page", 0), (b.get("bbox") or [0, 0, 0, 0])[1], (b.get("bbox") or [0, 0, 0, 0])[0]))
groups: list[dict] = []
next_id = 1
by_page: dict[int, list[dict]] = {}
for block in media:
by_page.setdefault(int(block.get("page", 0) or 0), []).append(block)
for page, page_media in by_page.items():
for block in page_media:
groups.append(_candidate_group_entry(f"group_{next_id:03d}", page, [block], "single_asset", ["single_asset"]))
next_id += 1
for start in range(len(page_media)):
for size in (2, 3):
chunk = page_media[start:start + size]
if len(chunk) != size:
continue
bboxes = [b.get("bbox", [0, 0, 0, 0]) for b in chunk]
heights = [_bbox_height(bb) for bb in bboxes]
centers_y = [_bbox_center_y(bb) for bb in bboxes]
if max(centers_y) - min(centers_y) > max(40.0, min(heights) * 0.35):
continue
if max(heights) - min(heights) > max(40.0, min(heights) * 0.4):
continue
gaps = []
ordered = sorted(chunk, key=lambda b: (b.get("bbox") or [0, 0, 0, 0])[0])
for left, right in zip(ordered, ordered[1:]):
lb = left.get("bbox", [0, 0, 0, 0])
rb = right.get("bbox", [0, 0, 0, 0])
gaps.append(max(0.0, rb[0] - lb[2]))
if any(gap > page_width * 0.08 for gap in gaps):
continue
group_type = "same_row_pair" if size == 2 else "same_row_triple"
evidence = ["same_page", "same_row_band", "size_similar", "tight_horizontal_gap"]
groups.append(_candidate_group_entry(f"group_{next_id:03d}", page, ordered, group_type, evidence))
next_id += 1
return groups
```
- [ ] **Step 3: Run the targeted test to verify it still fails for the right reason**
Run: `python -m pytest tests/test_ocr_figures.py -k group_first_matching_prefers_same_row_pair_over_single_asset -v`
Expected: still FAIL until the legend matching loop actually consumes candidate groups.
- [ ] **Step 4: Commit helper-only scaffolding**
```bash
git add paperforge/worker/ocr_figures.py tests/test_ocr_figures.py
git commit -m "refactor: add candidate figure group scaffolding"
```
## Task 3: Change Strict Matching From Asset-First To Group-First
**Files:**
- Modify: `paperforge/worker/ocr_figures.py`
- Test: `tests/test_ocr_figures.py`
- [ ] **Step 1: Replace raw asset candidate loop with candidate group loop**
In `build_figure_inventory()`, after collecting `assets`, build candidate groups:
```python
candidate_groups = _build_candidate_figure_groups_from_assets(assets, page_width=page_width)
used_asset_indices: set[int] = set()
used_asset_block_ids: set[int | str] = set()
```
Then replace the legend-scoring loop so it evaluates groups on the same page whose assets are all unconsumed.
- [ ] **Step 2: Keep group scoring generic and single-asset compatible**
Do not make `A and B` / `A–C` parsing a required condition in this phase. Instead, keep `single_asset` groups behavior as close as possible to the old `score_figure_match()` path, and let multi-asset groups compete only when their geometry is highly coherent.
Add a helper:
```python
def _score_legend_to_group(legend: dict, group: dict, *, caption_score: dict, page_width: float = 1200) -> dict:
if group.get("group_type") == "single_asset":
asset = group["media_blocks"][0]
return score_figure_match(
legend,
asset,
caption_score=caption_score,
anchor_supported=_has_anchor_supported_legend_context(legend),
caption_text_supported=_has_strong_explicit_caption_text(legend),
family_supported=False,
zone_supported=False,
)
# Multi-asset groups: score the group bbox and add a small coherence bonus.
# Keep this generic and geometry-only in phase 1.
```
- [ ] **Step 3: Materialize grouped strict matches without changing external shape**
When a candidate group wins, emit:
```python
entry = {
"figure_id": fig_id,
"legend_block_id": legend.get("block_id", ""),
"page": legend_page,
"text": legend_text,
"figure_number": fig_num,
"matched_assets": [
{"block_id": a.get("block_id", ""), "bbox": a.get("bbox", [0, 0, 0, 0])}
for a in winning_group["media_blocks"]
],
"cluster_bbox": winning_group["cluster_bbox"],
"group_type": winning_group["group_type"],
"group_evidence": winning_group["group_evidence"],
"confidence": match_score["score"],
"match_score": match_score,
"flags": ["group_first_match"] if len(winning_group["media_blocks"]) > 1 else [],
"caption_score": caption_score,
}
```
- [ ] **Step 4: Reserve all assets in a winning group together**
Update the consumption logic so all member `block_id`s are added to `used_asset_block_ids`. Filter all future candidate groups against this set.
- [ ] **Step 5: Run focused tests to verify the new grouped behavior**
Run:
```bash
python -m pytest tests/test_ocr_figures.py -k "group_first_matching_prefers_same_row_pair_over_single_asset or sequence_match_requires_at_least_one_asset_block_id" -v
```
Expected: PASS for the new group-first test and PASS for the existing safety tests.
- [ ] **Step 6: Commit group-first strict matching**
```bash
git add paperforge/worker/ocr_figures.py tests/test_ocr_figures.py
git commit -m "feat: match figure legends against candidate media groups"
```
## Task 4: Constrain Fallback Behavior So It Cannot Undo Grouped Matches
**Files:**
- Modify: `paperforge/worker/ocr_figures.py`
- Test: `tests/test_ocr_figures.py`
- [ ] **Step 1: Write a failing regression test for fallback stealing grouped assets**
Add:
```python
def test_sequential_fallback_does_not_split_grouped_assets() -> None:
from paperforge.worker.ocr_figures import build_figure_inventory
blocks = [
{"block_id": 1, "role": "figure_caption", "text": "Fig. 2 A and B, paired figure.", "page": 3, "bbox": [80, 120, 420, 210], "marker_signature": {"type": "figure_number"}, "zone": "display_zone", "style_family": "legend_like"},
{"block_id": 2, "role": "media_asset", "raw_label": "image", "page": 3, "bbox": [450, 120, 780, 520]},
{"block_id": 3, "role": "media_asset", "raw_label": "image", "page": 3, "bbox": [805, 120, 1130, 520]},
{"block_id": 4, "role": "figure_caption", "text": "Fig. 3 Single figure.", "page": 4, "bbox": [80, 120, 420, 210], "marker_signature": {"type": "figure_number"}, "zone": "display_zone", "style_family": "legend_like"},
]
inventory = build_figure_inventory(blocks, page_width=1200)
matched = {item["figure_number"]: item for item in inventory["matched_figures"]}
assert [a["block_id"] for a in matched[2]["matched_assets"]] == [2, 3]
assert 3 not in matched
```
- [ ] **Step 2: Run test to verify it fails if fallback still steals assets**
Run: `python -m pytest tests/test_ocr_figures.py -k sequential_fallback_does_not_split_grouped_assets -v`
Expected: FAIL before the fallback guard is added.
- [ ] **Step 3: Guard sequential fallback against grouped ownership**
Adjust fallback setup so `sorted_asts` excludes every asset whose `block_id` is already present in any grouped or single strict match.
- [ ] **Step 4: Restrict unresolved cluster creation to truly unclaimed assets**
Ensure `_media_clusters(unmatched_assets, ...)` sees only assets not already claimed by any grouped match.
- [ ] **Step 5: Guard `_promote_sequence_matches()` against grouped ownership conflicts**
Also ensure `_promote_sequence_matches()` does not create promoted matches that conflict with already claimed group assets, and does not promote empty-asset entries unless the existing contract explicitly allows sequence-style outcomes without assets.
- [ ] **Step 6: Run focused fallback tests**
Run:
```bash
python -m pytest tests/test_ocr_figures.py -k "sequential_fallback_does_not_split_grouped_assets or strict_layer_promotes_contiguous_legends_with_ordered_assets" -v
```
Expected: PASS.
- [ ] **Step 7: Commit fallback hardening**
```bash
git add paperforge/worker/ocr_figures.py tests/test_ocr_figures.py
git commit -m "fix: prevent fallback from splitting grouped figure assets"
```
## Task 5: Verify Reader Layer Accepts Grouped Strict Matches
**Files:**
- Modify: `tests/test_ocr_figure_reader.py`
- Test: `tests/test_ocr_figure_reader.py`
- [ ] **Step 1: Write a failing reader test for grouped strict inventory**
Add:
```python
def test_reader_materializes_grouped_strict_match_as_single_visual_group() -> None:
from paperforge.worker.ocr_figure_reader import synthesize_reader_figures
strict_inventory = {
"figure_legends": [],
"matched_figures": [
{
"figure_number": 2,
"legend_block_id": 10,
"page": 3,
"text": "Fig. 2 A and B, paired figure.",
"matched_assets": [
{"block_id": 20, "bbox": [100, 100, 300, 300]},
{"block_id": 21, "bbox": [320, 100, 520, 300]},
],
"match_score": {"score": 0.82, "decision": "matched", "evidence": ["same_row_pair"]},
"caption_score": {"score": 0.9},
}
],
"held_figures": [],
"ambiguous_figures": [],
"unmatched_legends": [],
"unresolved_clusters": [],
}
payload = synthesize_reader_figures(strict_inventory, structured_blocks=[])
rf = payload["reader_figures"][0]
assert rf["figure_number"] == 2
assert rf["visual_groups"][0]["asset_block_ids"] == [20, 21]
```
- [ ] **Step 2: Run test to verify current reader behavior**
Run: `python -m pytest tests/test_ocr_figure_reader.py -k grouped_strict_match_as_single_visual_group -v`
Expected: PASS or targeted FAIL only if grouped metadata reveals a normalization incompatibility.
- [ ] **Step 3: If needed, apply the minimal normalization fix**
Only if the test fails, adjust `_asset_ids_from_item()` or `_materialize_reader_figure()` so multi-asset strict matches remain one reader figure with one visual group.
- [ ] **Step 4: Run reader tests**
Run: `python -m pytest tests/test_ocr_figure_reader.py -v --tb=short`
Expected: PASS.
- [ ] **Step 5: Commit reader compatibility work**
```bash
git add tests/test_ocr_figure_reader.py paperforge/worker/ocr_figure_reader.py
git commit -m "test: verify reader figures preserve grouped strict matches"
```
## Task 6: Add Health Visibility For Grouped Matches
**Files:**
- Modify: `paperforge/worker/ocr_health.py`
- Test: `tests/test_ocr_figures.py`
- [ ] **Step 1: Add grouped-match counters in health report**
Add derived metrics:
```python
grouped_figure_match_count = sum(
1 for mf in figure_inventory.get("matched_figures", []) if len(mf.get("matched_assets", [])) > 1
)
single_asset_figure_match_count = sum(
1 for mf in figure_inventory.get("matched_figures", []) if len(mf.get("matched_assets", [])) == 1
)
```
Add to report:
```python
"grouped_figure_match_count": grouped_figure_match_count,
"single_asset_figure_match_count": single_asset_figure_match_count,
```
- [ ] **Step 2: Add a small health test**
Add a unit test that feeds a fake inventory with one grouped match and one single match into `build_ocr_health()` and verifies both counters.
- [ ] **Step 3: Run targeted health test**
Run: `python -m pytest tests/test_ocr_figures.py -k grouped_figure_match_count -v`
Expected: PASS.
- [ ] **Step 4: Commit health visibility**
```bash
git add paperforge/worker/ocr_health.py tests/test_ocr_figures.py
git commit -m "feat: report grouped figure match health metrics"
```
## Task 7: Shadow-Check Real Papers Without Changing Gold Expectations
**Files:**
- Modify: none
- Test: `tests/test_ocr_real_paper_regressions.py`
- Test: `tests/test_ocr_real_paper_contract.py`
- [ ] **Step 1: Run deterministic regression suite without changing gold expectations**
Run:
```bash
python -m pytest tests/test_ocr_real_paper_regressions.py -v --tb=short
```
Expected: PASS with no deterministic gold regressions.
- [ ] **Step 2: Run observational live-paper compare on one known multi-panel paper and one clean control**
Run:
```bash
$env:PAPERFORGE_REAL_OCR_VAULT='D:\L\OB\Literature-hub'; $env:PAPERFORGE_REAL_OCR_KEYS='6FGDBFQN,2GN9LMCW'; python -m pytest tests/test_ocr_real_paper_contract.py -v --tb=short
```
Expected: observational only. Do not tighten AJR ownership expectations in this phase.
- [ ] **Step 3: Record grouped-match observations, but do not edit expectations**
If grouped figure counts or reader outputs improve on live artifacts, note that in the implementation summary only. Do not modify `6FGDBFQN` gold ownership gates.
## Task 8: Full Verification Across Unit, Fixture, and Gold Gates
**Files:**
- Modify: none unless failures require targeted fixes
- Test: `tests/test_ocr_figures.py`
- Test: `tests/test_ocr_figure_reader.py`
- Test: `tests/test_ocr_real_paper_audit_contracts.py`
- Test: `tests/test_ocr_trace_vs_expectations.py`
- Test: `tests/test_ocr_real_paper_regressions.py`
- [ ] **Step 1: Run focused unit tests for figure inventory**
Run:
```bash
python -m pytest tests/test_ocr_figures.py -v --tb=short
```
Expected: PASS.
- [ ] **Step 2: Run reader tests**
Run:
```bash
python -m pytest tests/test_ocr_figure_reader.py -v --tb=short
```
Expected: PASS.
- [ ] **Step 3: Run fixture-backed OCR gold verification**
Run:
```bash
python -m pytest tests/test_ocr_real_paper_audit_contracts.py tests/test_ocr_trace_vs_expectations.py tests/test_ocr_real_paper_regressions.py -v --tb=short
```
Expected: all deterministic gold tests PASS; existing env-dependent live-vault tests may remain skipped.
- [ ] **Step 4: Spot-check live compare for AJR and one control paper**
Run:
```bash
$env:PAPERFORGE_REAL_OCR_VAULT='D:\L\OB\Literature-hub'; $env:PAPERFORGE_REAL_OCR_KEYS='6FGDBFQN,2GN9LMCW'; python -m pytest tests/test_ocr_real_paper_contract.py -v --tb=short
```
Expected: PASS or actionable flagged output only if the live vault has stale derived artifacts that need rebuild.
- [ ] **Step 5: Commit final verification-safe fixes**
```bash
git add .
git commit -m "feat: promote group-first figure inventory matching"
```
## Spec Coverage Check
- Fix the structural problem where grouped media exists in code but not as the default match unit: covered by Tasks 2-4.
- Preserve generic behavior for most papers and avoid AJR-only hacks: covered by candidate-group design and constraints in Tasks 2-4.
- Keep reader/render contracts stable: covered by Task 5.
- Improve observability and auditability: covered by Task 6.
- Prove the result on the deterministic gold suite and shadow-check live papers without widening scope: covered by Tasks 7-8.
## Placeholder Scan
- No `TODO`, `TBD`, or “appropriate handling” placeholders remain.
- Every code-changing task includes target file paths, test commands, and concrete code sketches.
## Type/Name Consistency Check
- Group builder terminology is consistent: `candidate group`, `group_type`, `asset_block_ids`, `matched_assets`.
- Downstream compatibility is preserved by keeping `matched_assets` as the externally consumed shape.
## Execution Handoff
**Plan complete and saved to `docs/superpowers/plans/2026-06-15-group-first-figure-inventory-plan.md`. Two execution options:**
**1. Subagent-Driven (recommended)** - I dispatch a fresh subagent per task, review between tasks, fast iteration
**2. Inline Execution** - Execute tasks in this session using executing-plans, batch execution with checkpoints
**Which approach?**

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# OCR-v2 Unified Close-Out Implementation Plan
> **For agentic workers:** REQUIRED SUB-SKILL: Use superpowers:subagent-driven-development (recommended) or superpowers:executing-plans to implement this plan task-by-task. Steps use checkbox (`- [ ]`) syntax for tracking.
**Goal:** Finish the current OCR-v2 close-out pass by removing useless preproof cover page 1 at the structured-block layer, conservatively tightening tail/post-reference cleanup, verifying the branch on the 8 gold papers plus up to 10 targeted vault papers, then rewriting the residual picture and deciding whether figure-group work is truly next.
**Architecture:** Keep the existing OCR-v2 anchor-first pipeline. Make one early filtering change in the structured-block build path so true preproof cover pages never enter document normalization, then make conservative no-damage changes in `ocr_document.py` so tail/post-reference handling preserves valid structure instead of trying to perfect `backmatter_heading` taxonomy. After code changes, run a bounded rebuild/audit pass on the 8 gold fixtures plus a deliberately chosen 10-paper vault sample and use that evidence to update the project trackers and decide whether to reopen figure-group work.
**Tech Stack:** Python 3.14, existing `paperforge.worker` OCR pipeline, pytest, existing rebuild scripts, repo-local audit helpers under `.opencode/skills/paperforge-development/`, vault OCR artifacts.
---
## File Structure
- Modify: `paperforge/worker/ocr_blocks.py`
- Owns structured-block build flow; add the preproof page-1 drop before document normalization.
- Modify: `paperforge/worker/ocr_document.py`
- Owns conservative tail/post-reference cleanup behavior.
- Modify: `tests/test_ocr_document.py`
- Owns document-pipeline and post-reference regression coverage.
- Modify: `tests/test_ocr_real_paper_regressions.py`
- Owns production-path replay checks on known papers.
- Modify: `tests/fixtures/ocr_real_papers/DWQQK2YB/expectations.json`
- Update only if post-fix visual truth proves the current expectations are stale.
- Modify: `tests/fixtures/ocr_real_papers/coverage_manifest.json`
- Record any extra verification-sample metadata only if the existing manifest is the cleanest place to note layout coverage.
- Modify: `project/current/ocr-error-root-cause-fix-queue.md`
- Write back the final measured residual summary.
- Modify: `PROJECT-MANAGEMENT.md`
- Record what the unified close-out pass fixed and what remains.
---
### Task 1: Lock Preproof-Drop And Conservative Tail Cleanup With Red Tests
**Files:**
- Modify: `tests/test_ocr_document.py`
- Modify: `tests/test_ocr_real_paper_regressions.py`
- [ ] **Step 1: Add a unit test for preproof page-1 removal**
Add this test to `tests/test_ocr_document.py` or the nearest structured/document pipeline test section:
```python
def test_preproof_cover_page_one_is_dropped_before_document_normalization() -> None:
from paperforge.worker.ocr_blocks import build_structured_blocks
raw_blocks = [
{
"paper_id": "DWTEST",
"block_id": "p1_preproof",
"page": 1,
"text": "Journal Pre-proof",
"block_content": "Journal Pre-proof",
"raw_label": "text",
"block_label": "text",
"marker_signature": {"type": "preproof_marker"},
"bbox": [80, 100, 600, 180],
"page_width": 1200,
"page_height": 1600,
},
{
"paper_id": "DWTEST",
"block_id": "p1_pii",
"page": 1,
"text": "PII: S1234-5678(26)00001-2",
"block_content": "PII: S1234-5678(26)00001-2",
"raw_label": "text",
"block_label": "text",
"marker_signature": {"type": "none"},
"bbox": [80, 240, 700, 300],
"page_width": 1200,
"page_height": 1600,
},
{
"paper_id": "DWTEST",
"block_id": "p2_title",
"page": 2,
"text": "Real Article Title",
"block_content": "Real Article Title",
"raw_label": "paragraph_title",
"block_label": "paragraph_title",
"marker_signature": {"type": "none"},
"bbox": [80, 120, 800, 190],
"page_width": 1200,
"page_height": 1600,
},
]
structured, _doc = build_structured_blocks(raw_blocks, source_metadata={"title": "Real Article Title"})
pages = {int(b.get("page", 0) or 0) for b in structured}
assert 1 not in pages
assert 2 in pages
```
- [ ] **Step 2: Add a non-preproof guard test**
Still in `tests/test_ocr_document.py`, add:
```python
def test_non_preproof_page_one_is_not_dropped() -> None:
from paperforge.worker.ocr_blocks import build_structured_blocks
raw_blocks = [
{
"paper_id": "OKTEST",
"block_id": "p1_title",
"page": 1,
"text": "A normal paper title",
"block_content": "A normal paper title",
"raw_label": "paragraph_title",
"block_label": "paragraph_title",
"marker_signature": {"type": "none"},
"bbox": [80, 120, 900, 200],
"page_width": 1200,
"page_height": 1600,
}
]
structured, _doc = build_structured_blocks(raw_blocks, source_metadata={"title": "A normal paper title"})
pages = {int(b.get("page", 0) or 0) for b in structured}
assert 1 in pages
```
- [ ] **Step 3: Add a conservative tail cleanup regression**
Add this test to `tests/test_ocr_document.py`:
```python
def test_post_reference_plain_heading_stays_heading_not_backmatter_taxonomy_upgrade() -> None:
from paperforge.worker.ocr_document import normalize_document_structure
blocks = [
{"block_id": "refs", "page": 12, "seed_role": "reference_heading", "text": "References", "bbox": [80, 900, 340, 960]},
{"block_id": "r1", "page": 12, "seed_role": "reference_item", "text": "[1] First ref", "bbox": [80, 1000, 960, 1080]},
{"block_id": "bio_h", "page": 13, "seed_role": "subsection_heading", "text": "Biographies", "bbox": [80, 120, 380, 180]},
{"block_id": "bio_b", "page": 13, "seed_role": "body_paragraph", "text": "Author A received the PhD degree in 2015.", "bbox": [80, 220, 980, 320]},
]
_doc, normalized = normalize_document_structure(blocks)
by_id = {b["block_id"]: b for b in normalized}
assert by_id["bio_h"]["role"] in {"subsection_heading", "backmatter_heading"}
assert by_id["bio_b"]["role"] != "unknown_structural"
```
- [ ] **Step 4: Add a production-path DW regression that expects page 1 to disappear**
In `tests/test_ocr_real_paper_regressions.py`, replace the old preproof-frontmatter expectation with:
```python
def test_dwqqk2yb_preproof_page_one_is_absent_from_structured_blocks(tmp_path: Path) -> None:
result = replay_production_pipeline("DWQQK2YB", tmp_path)
blocks = result["structured_blocks"]
assert not any(int(b.get("page", 0) or 0) == 1 for b in blocks)
```
- [ ] **Step 5: Run the focused red tests**
Run: `python -m pytest tests/test_ocr_document.py -k "preproof or post_reference_plain_heading" -v --tb=short`
Run: `python -m pytest tests/test_ocr_real_paper_regressions.py -k "dwqqk2yb_preproof_page_one" -v --tb=short`
Expected: FAIL until the implementation lands.
- [ ] **Step 6: Commit the failing tests only**
```bash
git add tests/test_ocr_document.py tests/test_ocr_real_paper_regressions.py
git commit -m "test: lock unified OCR close-out regressions"
```
---
### Task 2: Drop Confident Preproof Cover Page 1 At The Structured-Block Layer
**Files:**
- Modify: `paperforge/worker/ocr_blocks.py`
- Test: `tests/test_ocr_document.py`
- [ ] **Step 1: Add a narrow helper for preproof cover-page detection**
Near `build_structured_blocks()` helpers in `paperforge/worker/ocr_blocks.py`, add:
```python
def _is_confident_preproof_cover_page(blocks: list[dict], page: int) -> bool:
if page != 1:
return False
page_blocks = [b for b in blocks if int(b.get("page", 0) or 0) == page]
if not page_blocks:
return False
marker_hits = 0
noisy_cover_hits = 0
useful_heading_hits = 0
for block in page_blocks:
marker_type = str((block.get("marker_signature") or {}).get("type") or "")
text = str(block.get("text") or block.get("block_content") or "").strip().lower()
raw_label = str(block.get("raw_label") or block.get("block_label") or "")
if marker_type == "preproof_marker":
marker_hits += 1
if any(token in text for token in ("journal pre-proof", "pre-proof", "pii:", "accepted date", "published by elsevier")):
noisy_cover_hits += 1
if raw_label == "paragraph_title" and len(text.split()) >= 4 and "pre-proof" not in text:
useful_heading_hits += 1
return marker_hits >= 1 and noisy_cover_hits >= 2 and useful_heading_hits == 0
```
- [ ] **Step 2: Filter page 1 before document normalization**
In `build_structured_blocks()`, after the initial structured row shaping and before `normalize_document_structure()` runs, add:
```python
if _is_confident_preproof_cover_page(structured_blocks, 1):
structured_blocks = [
block for block in structured_blocks
if int(block.get("page", 0) or 0) != 1
]
```
- [ ] **Step 3: Make the filter self-documenting in evidence/debug metadata**
Add a small debug note so dropped-page behavior is traceable during local debugging:
```python
for block in structured_blocks:
if int(block.get("page", 0) or 0) == 2:
block.setdefault("evidence", []).append("page_1_preproof_cover_dropped_upstream")
break
```
- [ ] **Step 4: Run focused tests until green**
Run: `python -m pytest tests/test_ocr_document.py -k "preproof_cover_page_one or non_preproof_page_one" -v --tb=short`
Run: `python -m pytest tests/test_ocr_real_paper_regressions.py -k "dwqqk2yb_preproof_page_one" -v --tb=short`
Expected: PASS.
- [ ] **Step 5: Commit**
```bash
git add paperforge/worker/ocr_blocks.py tests/test_ocr_document.py tests/test_ocr_real_paper_regressions.py
git commit -m "fix: drop preproof cover page one before OCR normalization"
```
---
### Task 3: Tighten Tail/Post-Reference Cleanup Conservatively
**Files:**
- Modify: `paperforge/worker/ocr_document.py`
- Test: `tests/test_ocr_document.py`
- [ ] **Step 1: Add a helper that recognizes explicit backmatter-body evidence only**
Near `_exclude_tail_nonref_from_body_flow()` in `paperforge/worker/ocr_document.py`, keep the helper deliberately small:
```python
def _looks_like_backmatter_body_text(text: str) -> bool:
lower = text.lower()
markers = (
"conflict of interest",
"declaration",
"publisher",
"author contributions",
"funding",
"acknowledg",
"data availability",
"ethics",
"supplement",
)
return any(marker in lower for marker in markers)
```
- [ ] **Step 2: Make `_exclude_tail_nonref_from_body_flow()` preserve ordinary prose by default**
Replace the broad conversion logic with:
```python
if not _looks_like_backmatter_body_text(text):
continue
```
and keep the role rewrite only for blocks that pass the helper.
- [ ] **Step 3: Keep post-reference normalization from damaging object-adjacent structure**
Inside `_normalize_backmatter_roles_after_boundary()`, add a preserve set and skip conversion for those roles:
```python
_POST_REF_PRESERVE_ROLES = {
"figure_inner_text",
"figure_caption",
"figure_caption_candidate",
"table_caption",
"table_caption_candidate",
"media_asset",
"table_html",
}
```
and use:
```python
if block.get("role") in _POST_REF_PRESERVE_ROLES:
continue
```
- [ ] **Step 4: Avoid aggressive heading taxonomy expansion**
Do not add a large phrase list for `backmatter_heading`. Instead, keep valid `section_heading` / `subsection_heading` / `sub_subsection_heading` intact when the current evidence already supports them.
Use this conservative guard in the normalization branch:
```python
if block.get("role") in {"section_heading", "subsection_heading", "sub_subsection_heading"}:
continue
```
- [ ] **Step 5: Run focused tests until green**
Run: `python -m pytest tests/test_ocr_document.py -k "tail_nonref or backmatter or post_reference_plain_heading" -v --tb=short`
Expected: PASS.
- [ ] **Step 6: Commit**
```bash
git add paperforge/worker/ocr_document.py tests/test_ocr_document.py
git commit -m "fix: conservatively tighten OCR tail and post-reference cleanup"
```
---
### Task 4: Run The Bounded Rebuild And Choose The 10-Paper Expansion Sample
**Files:**
- Modify if needed: `tests/fixtures/ocr_real_papers/coverage_manifest.json`
- Reference: vault OCR store under `D:/L/OB/Literature-hub/System/PaperForge/ocr/`
- [ ] **Step 1: Rebuild the 8 gold papers first**
Run:
```bash
python scripts/dev/ocr_rebuild_paper.py CAQNW9Q2 DWQQK2YB TSCKAVIS A8E7SRVS K7R8PEKW 6FGDBFQN SAN9AYVR 2GN9LMCW
```
Expected: refreshed structured blocks, document structure, render output, and trace artifacts for the gold set.
- [ ] **Step 2: Pick up to 10 extra vault papers by layout class**
Use the vault OCR inventory plus existing audit knowledge to choose up to 10 papers that cover these classes:
```text
1. preproof cover page
2. same-page body/reference split
3. biography/backmatter tail
4. multi-panel figure
5. narrow-caption or side-caption figure
6. table-heavy layout
7. publisher margin-noise layout
```
Write the chosen keys into your working notes or `coverage_manifest.json` only if that file is already the cleanest coverage ledger.
- [ ] **Step 3: Rebuild the 10 selected vault papers**
Run:
```bash
python scripts/dev/ocr_rebuild_paper.py <KEY1> <KEY2> <KEY3> <KEY4> <KEY5> <KEY6> <KEY7> <KEY8> <KEY9> <KEY10>
```
Expected: the bounded sample is rebuilt from current code, without opening a full-vault rebuild loop.
- [ ] **Step 4: Refresh audit artifacts for the selected papers**
For each selected key, run:
```bash
python .opencode/skills/paperforge-development/scripts/ocr_truth_audit.py <KEY> --source-root D:/L/OB/Literature-hub/System/PaperForge/ocr --mode high-risk --refresh-artifacts
```
Expected: refreshed high-risk audit helper outputs without manual truth loss.
- [ ] **Step 5: Commit only if coverage metadata changed**
If you updated `tests/fixtures/ocr_real_papers/coverage_manifest.json`, commit it:
```bash
git add tests/fixtures/ocr_real_papers/coverage_manifest.json
git commit -m "docs: record bounded OCR close-out verification sample"
```
If no tracked metadata changed, skip this commit.
---
### Task 5: Regressions, Truth Updates, And Residual Rewrite
**Files:**
- Modify: `tests/fixtures/ocr_real_papers/DWQQK2YB/expectations.json`
- Modify: `project/current/ocr-error-root-cause-fix-queue.md`
- Modify: `PROJECT-MANAGEMENT.md`
- [ ] **Step 1: Run the deterministic regression suite**
Run:
```bash
python -m pytest tests/test_ocr_document.py tests/test_ocr_real_paper_regressions.py tests/test_ocr_roles.py tests/test_ocr_layout_first_regressions.py -v --tb=short
```
Expected: green or improved close-out gates for the touched areas.
- [ ] **Step 2: Update expectations only where visual truth proves the old truth is stale**
If DW expectations still mention page-1 preproof content after the drop, update the relevant `tests/fixtures/ocr_real_papers/DWQQK2YB/expectations.json` entries so they stop asserting the removed cover page.
Use this pattern only when justified:
```json
{
"text_contains": "Real article title",
"expected_role": "paper_title",
"expected_zone": "frontmatter_main_zone",
"notes": "Updated after structured-layer preproof page-1 drop; visual audit confirms page 1 is disposable cover content."
}
```
- [ ] **Step 3: Re-run the high-risk diff audit on touched papers**
Run for the gold papers most affected by this pass and for the newly selected sample papers:
```bash
python .opencode/skills/paperforge-development/scripts/diff_audit.py DWQQK2YB --source-root D:/L/OB/Literature-hub/System/PaperForge/ocr --audit-root D:/L/Med/Research/99_System/LiteraturePipeline/github-release/audit
```
Repeat for CAQ and any newly selected papers where the current pass changed outputs materially.
- [ ] **Step 4: Rewrite the residual summary from evidence**
Update `project/current/ocr-error-root-cause-fix-queue.md` and `PROJECT-MANAGEMENT.md` with:
```md
### Unified Close-Out Pass Result (2026-06-17)
- What changed: preproof cover page 1 is dropped before normalization; tail/post-reference cleanup is more conservative
- Verification scope: 8 gold papers + up to 10 targeted vault papers
- Result: [fill with measured residual delta after rerun]
- Remaining document-structure residuals: [measured list]
- Remaining figure-ownership residuals: [measured list]
- Decision: [state whether figure-group is now the dominant blocker]
```
- [ ] **Step 5: Commit**
```bash
git add tests/fixtures/ocr_real_papers/DWQQK2YB/expectations.json project/current/ocr-error-root-cause-fix-queue.md PROJECT-MANAGEMENT.md
git commit -m "docs: record unified OCR close-out pass results"
```
---
### Task 6: Figure-Group Decision Gate
**Files:**
- Modify if needed: `project/current/ocr-v2-closeout-priority.md`
- Modify if needed: `PROJECT-MANAGEMENT.md`
- [ ] **Step 1: Compare residual category totals after the bounded audit pass**
Build a simple decision table from the refreshed audit results:
```text
document_structure_residuals = ?
truth_or_expectation_residuals = ?
figure_ownership_residuals = ?
```
Only use measured post-pass counts.
- [ ] **Step 2: Decide whether figure-group work is now dominant**
Use this rule:
```text
Reopen group-first figure inventory only if figure ownership is the largest stable remaining class
and it appears across more than one paper/layout family in the bounded sample.
```
- [ ] **Step 3: Write the decision back to the active priority file if it changes**
If figure-group becomes the next dominant blocker, update `project/current/ocr-v2-closeout-priority.md` and `PROJECT-MANAGEMENT.md` to say so explicitly. If it does not, update them to keep the branch on the current close-out/verification path.
- [ ] **Step 4: Commit only if a tracked priority file changed**
```bash
git add project/current/ocr-v2-closeout-priority.md PROJECT-MANAGEMENT.md
git commit -m "docs: update OCR close-out next-step decision"
```
Skip the commit if no tracked priority files changed.
---
## Self-Review
- Spec coverage: this plan covers every section of the approved unified spec: preproof page-1 drop, conservative tail cleanup, bounded 8+10 verification, residual write-back, and figure-group decision gate.
- Placeholder scan: no `TODO`, `TBD`, or unresolved implementation placeholders remain in the task steps.
- Type consistency: the plan uses existing seams only (`build_structured_blocks`, `normalize_document_structure`, `_exclude_tail_nonref_from_body_flow`, `_normalize_backmatter_roles_after_boundary`, `replay_production_pipeline`, `ocr_rebuild_paper.py`, `ocr_truth_audit.py`, `diff_audit.py`) and does not invent a second implementation track.
Plan complete and saved to `docs/superpowers/plans/2026-06-17-ocr-v2-closeout-single-plan.md`. Two execution options:
**1. Subagent-Driven (recommended)** - I dispatch a fresh subagent per task, review between tasks, fast iteration
**2. Inline Execution** - Execute tasks in this session using executing-plans, batch execution with checkpoints
Which approach?

523
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# DWQ Repair And Completeness Phase Implementation Plan
> **For agentic workers:** REQUIRED SUB-SKILL: Use superpowers:subagent-driven-development (recommended) or superpowers:executing-plans to implement this plan task-by-task. Steps use checkbox (`- [ ]`) syntax for tracking.
**Goal:** Clean stale OCR tracking files first, then repair the remaining real DWQQK2YB support-routing and figure-ownership issues, rebuild affected derived artifacts, and leave the completeness-check layer fully planned as the next slice.
**Architecture:** This phase has one hard rule: fix the repo's current truth files before touching behavior, so later work stops inheriting stale issue narratives. After the cleanup, make two narrow production repairs in the existing OCR-v2 pipeline: one in frontmatter support routing and one in figure ownership partition/claim logic, then rebuild the affected paper artifacts and update the docs again from measured results. The completeness-check feature remains planning-only in this phase and is carried forward as the next explicit implementation topic.
**Tech Stack:** Python 3.14, pytest, existing `paperforge.worker` OCR pipeline, `scripts/dev/ocr_rebuild_paper.py`, audit artifacts under `audit/`, real-paper replay regressions.
---
## File Structure
- Modify: `project/current/ocr-v2-closeout-priority.md`
- Active next-step truth source; must stop claiming already-fixed P0-P2 issues are open.
- Modify: `project/current/ocr-v2-remaining-issues-2026-06-18.md`
- Residual issue snapshot; must be rewritten from actual post-9.7 state.
- Modify: `PROJECT-MANAGEMENT.md`
- Session log and current truth surface; must record file cleanup, DWQ repairs, rebuild results, and next planned completeness topic.
- Modify: `paperforge/worker/ocr_roles.py`
- Frontmatter support routing seam for first-surviving-page support-like blocks.
- Modify: `paperforge/worker/ocr_figures.py`
- Figure candidate-group partition and local asset claiming seam for DWQQK2YB ownership repair.
- Modify: `tests/test_ocr_roles.py`
- Unit-level frontmatter support regression coverage.
- Modify: `tests/test_ocr_figures.py`
- Unit-level ownership/partition regression coverage.
- Modify: `tests/test_ocr_real_paper_regressions.py`
- Real-paper DWQQK2YB ownership and support-routing checks.
- Use: `scripts/dev/ocr_rebuild_paper.py`
- Canonical derived-artifact rebuild entry point after the code fixes land.
- Preserve reference spec: `docs/superpowers/specs/2026-06-18-ocr-completeness-check-design.md`
- Carry forward into the end-of-phase planning/doc update; no production-code changes in this phase.
---
### Task 1: Clean Stale Truth Files First
**Files:**
- Modify: `project/current/ocr-v2-closeout-priority.md`
- Modify: `project/current/ocr-v2-remaining-issues-2026-06-18.md`
- Modify: `PROJECT-MANAGEMENT.md`
- [ ] **Step 1: Write the failing doc-truth regression note as a checklist in the current files**
At the top of your work buffer, use this checklist to drive the edits:
```text
- remove already-fixed DW post-drop frontmatter item as an active open bug
- remove or downgrade stale `media_asset -> body_paragraph` as a live blocker when latest analysis says it was stale audit truth
- stop treating DW biography mismatch as active if the current replay/audit state already moved
- keep DW frontmatter support routing open
- keep DW figure ownership open
- keep completeness check as next planned work
```
- [ ] **Step 2: Rewrite `project/current/ocr-v2-closeout-priority.md` so it no longer inherits stale blockers**
Make the following concrete edits:
```md
## Remaining blockers before merge:
- DWQQK2YB frontmatter support/equal-contribution routing on the first surviving page
- DWQQK2YB figure ownership on mixed post-reference figure pages
- derived artifact rebuild after ownership/support repairs
- completeness-check layer implementation (next slice)
```
Remove these stale or downgraded claims from that block unless fresh evidence re-proves them during this phase:
```md
- `media_asset → body_paragraph` (42 blocks) — zone/attribution work
- DW biography page mismatch
```
- [ ] **Step 3: Rewrite `project/current/ocr-v2-remaining-issues-2026-06-18.md` to reflect post-9.7 truth**
Replace the stale issue sections with a compact residual list shaped like this:
```md
## Active Residuals After 9.7
1. DWQQK2YB first-surviving-page support blocks still route inconsistently (`frontmatter_support` vs `frontmatter_noise` / body fallback)
2. DWQQK2YB figure ownership still over-claims or stays ambiguous on mixed post-reference figure pages
3. Backmatter heading taxonomy remains partially conservative by design; only fix if verified zone semantics require it
4. Completeness-check layer is specified and pending implementation
```
Delete or archive the old sections for:
- Issue 1: DW post-drop frontmatter not recognized
- Issue 3: figure inner label issue as a main active blocker
- Issue 4: publisher noise as a main active blocker
- Issue 6: DW biography mismatch as an active bug
- [ ] **Step 4: Add a new `PROJECT-MANAGEMENT.md` entry recording the truth-file cleanup before code changes**
Append a new entry skeleton using this exact shape:
```md
### 9.8 Task 1 — Active OCR Truth-File Cleanup (2026-06-18)
**Problem:** Active current-priority files still inherited stale pre-9.7 issue narratives, causing the next phase to start from wrong blockers.
**Root cause:** `project/current/` and `PROJECT-MANAGEMENT.md` were not fully reconciled after the P0-P2 close-out session.
**Fix:** Rewrote the active close-out priority and remaining-issues files so only real unresolved DWQQK2YB support-routing and ownership issues remain active; completeness-check stays as the next planned slice.
**Result:** Later implementation steps no longer inherit already-fixed P0-P2 failures as active blockers.
```
- [ ] **Step 5: Run a quick grep-based verification that the stale phrases are gone from active current files**
Run: `rg -n "DW Post-Drop Frontmatter Not Recognized|media_asset -> body_paragraph|DW Biography Page Mismatch" project/current PROJECT-MANAGEMENT.md`
Expected: only historical/project-log references remain; active current sections no longer present those as top open blockers.
- [ ] **Step 6: Commit the truth-file cleanup first**
```bash
git add project/current/ocr-v2-closeout-priority.md project/current/ocr-v2-remaining-issues-2026-06-18.md PROJECT-MANAGEMENT.md
git commit -m "docs: align OCR active issues after 9.7 close-out"
```
---
### Task 2: Lock DWQQK2YB Frontmatter Support Routing With Red Tests
**Files:**
- Modify: `tests/test_ocr_roles.py`
- Modify: `tests/test_ocr_real_paper_regressions.py`
- [ ] **Step 1: Write the failing unit test for first-surviving-page equal-contribution support**
Add this to `tests/test_ocr_roles.py` near the page-1 frontmatter support tests:
```python
def test_first_surviving_page_equal_contribution_text_routes_to_frontmatter_support() -> None:
from paperforge.worker.ocr_roles import assign_block_role
title = {
"page": 2,
"block_label": "text",
"raw_label": "text",
"block_content": "Real Article Title",
"text": "Real Article Title",
"block_bbox": [80, 140, 900, 190],
"page_width": 1200,
"page_height": 1600,
}
support = {
"page": 2,
"block_label": "text",
"raw_label": "text",
"block_content": "These authors contributed equally to this work.",
"text": "These authors contributed equally to this work.",
"block_bbox": [90, 760, 560, 805],
"page_width": 1200,
"page_height": 1600,
}
result = assign_block_role(support, page_blocks=[title, support], page_width=1200, page_height=1600)
assert result.role == "frontmatter_support"
```
- [ ] **Step 2: Write the failing unit test for first-surviving-page correspondence support**
Add this immediately after the previous test in `tests/test_ocr_roles.py`:
```python
def test_first_surviving_page_correspondence_text_routes_to_frontmatter_support() -> None:
from paperforge.worker.ocr_roles import assign_block_role
title = {
"page": 2,
"block_label": "text",
"raw_label": "text",
"block_content": "Real Article Title",
"text": "Real Article Title",
"block_bbox": [80, 140, 900, 190],
"page_width": 1200,
"page_height": 1600,
}
support = {
"page": 2,
"block_label": "text",
"raw_label": "text",
"block_content": "Corresponding author: person@example.edu",
"text": "Corresponding author: person@example.edu",
"block_bbox": [90, 820, 980, 900],
"page_width": 1200,
"page_height": 1600,
}
result = assign_block_role(support, page_blocks=[title, support], page_width=1200, page_height=1600)
assert result.role == "frontmatter_support"
```
- [ ] **Step 3: Write the failing real-paper regression for DW support lines**
Add this to `tests/test_ocr_real_paper_regressions.py` near the other DW tests:
```python
def test_dwqqk2yb_first_surviving_page_support_blocks_stay_frontmatter_support(tmp_path: Path) -> None:
result = replay_production_pipeline("DWQQK2YB", tmp_path)
page2_blocks = [b for b in result["structured_blocks"] if int(b.get("page", 0) or 0) == 2]
equal_block = next(b for b in page2_blocks if "contributed equally" in str(b.get("text") or "").lower())
corr_block = next(b for b in page2_blocks if "corresponding author" in str(b.get("text") or "").lower())
assert equal_block.get("role") == "frontmatter_support"
assert corr_block.get("role") == "frontmatter_support"
```
- [ ] **Step 4: Run the focused tests to verify red**
Run: `python -m pytest tests/test_ocr_roles.py -k "first_surviving_page and frontmatter_support" -v --tb=short`
Expected: FAIL because the current routing still leans on page-1 and text-prefix specials.
Run: `python -m pytest tests/test_ocr_real_paper_regressions.py -k "dwqqk2yb_first_surviving_page_support" -v --tb=short`
Expected: FAIL because DW support-like blocks still route inconsistently.
- [ ] **Step 5: Commit the failing tests only**
```bash
git add tests/test_ocr_roles.py tests/test_ocr_real_paper_regressions.py
git commit -m "test: lock DW frontmatter support regressions"
```
---
### Task 3: Implement DWQQK2YB Frontmatter Support Repair
**Files:**
- Modify: `paperforge/worker/ocr_roles.py`
- Test: `tests/test_ocr_roles.py`
- Test: `tests/test_ocr_real_paper_regressions.py`
- [ ] **Step 1: Add a helper for frontmatter-support-like text confirmation**
In `paperforge/worker/ocr_roles.py`, near the other frontmatter helpers, add:
```python
def _looks_like_frontmatter_support_text(text: str) -> bool:
lower = text.lower().strip()
return (
"corresponding author" in lower
or lower.startswith("correspondence")
or "contributed equally" in lower
or "share first authorship" in lower
)
```
- [ ] **Step 2: Route first-surviving-page support-like text before generic frontmatter noise/body fallback**
In the `raw_label == "text"` branch of `assign_block_role()` in `paperforge/worker/ocr_roles.py`, insert this block before the generic body fallback:
```python
bbox = block.get("block_bbox") or block.get("bbox") or [0, 0, 0, 0]
topish = len(bbox) >= 4 and bbox[1] <= page_height * 0.65
support_zone = zone in {"author_zone", "affiliation_zone", "journal_furniture_zone"}
if page_num in (1, 2) and topish and _looks_like_frontmatter_support_text(text):
return RoleAssignment(
role="frontmatter_support",
confidence=0.78,
evidence=[f"first-surviving-page support text: {text[:60]}"],
)
```
Keep the helper text-confirmatory only; this remains bounded to early-page support routing and should not become a generic full-document string rule.
- [ ] **Step 3: Run the focused tests to verify green**
Run: `python -m pytest tests/test_ocr_roles.py -k "first_surviving_page and frontmatter_support" -v --tb=short`
Expected: PASS.
Run: `python -m pytest tests/test_ocr_real_paper_regressions.py -k "dwqqk2yb_first_surviving_page_support" -v --tb=short`
Expected: PASS.
- [ ] **Step 4: Commit**
```bash
git add paperforge/worker/ocr_roles.py tests/test_ocr_roles.py tests/test_ocr_real_paper_regressions.py
git commit -m "fix: preserve DW frontmatter support on surviving page"
```
---
### Task 4: Lock DWQQK2YB Figure Ownership With Red Tests
**Files:**
- Modify: `tests/test_ocr_figures.py`
- Modify: `tests/test_ocr_real_paper_regressions.py`
- [ ] **Step 1: Write the failing unit test for caption-band-local asset partition**
Add this to `tests/test_ocr_figures.py` near the candidate-group tests:
```python
def test_partition_assets_by_caption_bands_keeps_assets_local_to_caption_band() -> None:
from paperforge.worker.ocr_figures import _partition_assets_by_caption_bands
captions = [
{"block_id": 101, "bbox": [700, 900, 1050, 960]},
{"block_id": 102, "bbox": [700, 1250, 1050, 1310]},
]
assets = [
{"block_id": 1, "bbox": [650, 200, 1050, 520]},
{"block_id": 2, "bbox": [650, 560, 1050, 820]},
{"block_id": 3, "bbox": [650, 980, 1050, 1180]},
]
parts = _partition_assets_by_caption_bands(captions, assets, page_height=1600)
assert [a["block_id"] for a in parts["101"]] == [1, 2]
assert [a["block_id"] for a in parts["102"]] == [3]
```
- [ ] **Step 2: Write the failing real-paper ownership regression for DW**
Add this to `tests/test_ocr_real_paper_regressions.py` next to `test_gold_figure_merge_ownership_contracts`:
```python
def test_dwqqk2yb_ownership_no_longer_mega_merges_same_page_assets(tmp_path: Path) -> None:
result = replay_production_pipeline("DWQQK2YB", tmp_path)
reader_payload = result["reader_payload"]
matched, ambiguous = _reader_figure_index(reader_payload)
fig2 = matched.get(2)
fig4 = matched.get(4)
assert fig2 is not None
assert len(fig2.get("asset_block_ids", [])) <= 3
assert fig4 is not None
assert len(fig4.get("asset_block_ids", [])) <= 3
assert 3 in matched or 3 in ambiguous
```
- [ ] **Step 3: Run the focused tests to verify red**
Run: `python -m pytest tests/test_ocr_figures.py -k "caption_bands" -v --tb=short`
Expected: FAIL if current partitioning does not keep same-page assets local enough.
Run: `python -m pytest tests/test_ocr_real_paper_regressions.py -k "dwqqk2yb_ownership_no_longer_mega_merges" -v --tb=short`
Expected: FAIL because Figure 2 / 4 currently over-claim and Figure 3 stays unresolved.
- [ ] **Step 4: Commit the failing tests only**
```bash
git add tests/test_ocr_figures.py tests/test_ocr_real_paper_regressions.py
git commit -m "test: lock DW figure ownership regressions"
```
---
### Task 5: Implement DWQQK2YB Figure Ownership Repair
**Files:**
- Modify: `paperforge/worker/ocr_figures.py`
- Test: `tests/test_ocr_figures.py`
- Test: `tests/test_ocr_real_paper_regressions.py`
- [ ] **Step 1: Use caption-band partitioning before broad same-page claiming**
In `build_figure_inventory()` in `paperforge/worker/ocr_figures.py`, after `page_caption_index` is built and before broad candidate scoring claims the whole page cluster, compute per-page partition maps:
```python
page_assets_index: dict[int, list[dict]] = {}
for asset in assets:
page_assets_index.setdefault(int(asset.get("page", 0) or 0), []).append(asset)
partitioned_assets_by_caption: dict[tuple[int, str], list[dict]] = {}
for page, captions in page_caption_index.items():
partitions = _partition_assets_by_caption_bands(captions, page_assets_index.get(page, []), page_height=1700)
for caption_id, caption_assets in partitions.items():
partitioned_assets_by_caption[(page, caption_id)] = caption_assets
```
- [ ] **Step 2: Restrict candidate groups to caption-local assets when partition evidence exists**
In the legend loop inside `build_figure_inventory()`, before appending a candidate group, insert this guard:
```python
local_assets = partitioned_assets_by_caption.get((legend_page, str(legend.get("block_id", ""))))
if local_assets:
local_ids = {a.get("block_id") for a in local_assets}
group_ids = set(group.get("asset_block_ids", []))
if not (group_ids & local_ids):
continue
```
This keeps same-page narrow-caption ownership local when partition evidence exists, without inventing a paper-specific branch.
- [ ] **Step 3: Keep unresolved over false mega-merge**
When no caption-local candidate remains after the partition guard, preserve ambiguity instead of claiming the highest broad page group. Use this shape where the weak/ambiguous path is built:
```python
ambiguous_figures.append(
{
"figure_number": fig_num,
"legend_block_id": legend.get("block_id", ""),
"asset_block_ids": [],
"candidate_asset_ids": [],
"page": legend_page,
}
)
continue
```
Only use this path when local partition evidence existed and no local group survived; do not broadly weaken all papers.
- [ ] **Step 4: Run the focused tests to verify green**
Run: `python -m pytest tests/test_ocr_figures.py -k "caption_bands" -v --tb=short`
Expected: PASS.
Run: `python -m pytest tests/test_ocr_real_paper_regressions.py -k "dwqqk2yb_ownership_no_longer_mega_merges or gold_figure_merge_ownership_contracts" -v --tb=short`
Expected: PASS, or the only remaining ambiguity is explicitly the guarded unresolved path and no false mega-merge remains.
- [ ] **Step 5: Commit**
```bash
git add paperforge/worker/ocr_figures.py tests/test_ocr_figures.py tests/test_ocr_real_paper_regressions.py
git commit -m "fix: constrain DW figure ownership to local caption bands"
```
---
### Task 6: Rebuild Derived Artifacts And Update Docs From Measured Results
**Files:**
- Run: `scripts/dev/ocr_rebuild_paper.py`
- Modify: `PROJECT-MANAGEMENT.md`
- Modify: `project/current/ocr-v2-closeout-priority.md`
- Modify: `project/current/ocr-v2-remaining-issues-2026-06-18.md`
- [ ] **Step 1: Rebuild the affected paper artifacts after the code repairs**
Run: `python scripts/dev/ocr_rebuild_paper.py --trace DWQQK2YB`
Expected: derived artifacts rebuilt, `block_trace.csv` regenerated, role/zone distributions printed.
- [ ] **Step 2: Run the targeted DW verification slice after rebuild**
Run: `python -m pytest tests/test_ocr_real_paper_regressions.py -k "DWQQK2YB" -v --tb=short`
Expected: support-routing and ownership checks now reflect rebuilt artifacts.
- [ ] **Step 3: Record the rebuild and measured repair outcome in `PROJECT-MANAGEMENT.md`**
Append a new entry using this shape:
```md
### 9.9 Task 2 — DWQQK2YB Support + Ownership Repair And Rebuild (2026-06-18)
**Problem:** After 9.7, DWQQK2YB still had one real frontmatter-support routing gap and one real same-page figure ownership gap; derived assets also lagged behind the repaired code path.
**Root cause:** early support routing still leaned on page-1/text-special-case logic, and same-page figure claiming treated broad page media clusters as one ownership pool.
**Fix:** promoted first-surviving-page frontmatter support lines using bounded support-text confirmation; constrained figure claiming to caption-local partition bands before broad same-page asset adoption; rebuilt DWQQK2YB derived artifacts with `scripts/dev/ocr_rebuild_paper.py`.
**Result:** DW support blocks remain in frontmatter support flow; figure ownership no longer mega-merges same-page assets; rebuilt artifacts now reflect the repaired logic.
**Test status:** [insert exact pytest and rebuild commands/results from this run].
```
- [ ] **Step 4: Rewrite the active current files again from the measured post-rebuild result**
Update:
- `project/current/ocr-v2-closeout-priority.md`
- `project/current/ocr-v2-remaining-issues-2026-06-18.md`
so they reflect only what remained true after the rebuild. If the DW support issue or the DW ownership issue is fully gone, remove it from active blockers. If one remains partial, rewrite it precisely rather than keeping the older broad wording.
- [ ] **Step 5: Commit**
```bash
git add PROJECT-MANAGEMENT.md project/current/ocr-v2-closeout-priority.md project/current/ocr-v2-remaining-issues-2026-06-18.md
git commit -m "docs: record DW repair and OCR rebuild results"
```
---
### Task 7: Carry Completeness Check Forward As The Next Explicit Slice
**Files:**
- Preserve: `docs/superpowers/specs/2026-06-18-ocr-completeness-check-design.md`
- Preserve: `docs/superpowers/specs/2026-06-18-dwq-repair-and-completeness-phase-design.md`
- Modify: `PROJECT-MANAGEMENT.md`
- [ ] **Step 1: Add a next-topic note pointing at the completeness-check spec**
In the latest active session block in `PROJECT-MANAGEMENT.md`, add a final line like:
```md
- Next topic: implement the fuzzy OCR completeness-check layer described in `docs/superpowers/specs/2026-06-18-ocr-completeness-check-design.md` after the DW repairs settle.
```
- [ ] **Step 2: Run one final sanity slice across the touched areas**
Run: `python -m pytest tests/test_ocr_roles.py tests/test_ocr_figures.py tests/test_ocr_real_paper_regressions.py -k "DWQQK2YB or frontmatter_support or ownership" -v --tb=short`
Expected: PASS, with no new regressions in the touched seams.
- [ ] **Step 3: Commit the final planning-state sync**
```bash
git add PROJECT-MANAGEMENT.md
git commit -m "docs: point OCR next slice at completeness checks"
```

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# OCR P0-P2 Layout Close-Out Implementation Plan
> **For agentic workers:** REQUIRED SUB-SKILL: Use superpowers:subagent-driven-development (recommended) or superpowers:executing-plans to implement this plan task-by-task. Steps use checkbox (`- [ ]`) syntax for tracking.
**Goal:** Fix the current OCR-v2 P0-P2 close-out issues by making first-surviving-page zone inference authoritative, tightening publisher edge-band noise handling, recovering residual figure inner text with layout evidence, and updating only the verified stale expectations.
**Architecture:** Keep the existing OCR-v2 anchor-first pipeline and patch the two narrow seams that are actually failing: `infer_zones()` in `ocr_document.py` and layout-first fallback classification in `ocr_roles.py`. Use TDD with focused unit tests first, then confirm the behavior against `DWQQK2YB`, `K7R8PEKW`/`A8E7SRVS`, and one control paper so the close-out fixes do not reopen the broader figure-group or page-state-machine work.
**Tech Stack:** Python 3.14, pytest, existing `paperforge.worker` OCR pipeline, fixture-backed real-paper replay tests, repo audit artifacts under `audit/`.
---
## File Structure
- Modify: `paperforge/worker/ocr_document.py`
- Owns `infer_zones()` and the first-page/frontmatter/body split logic.
- Modify: `paperforge/worker/ocr_roles.py`
- Owns layout-first fallback role assignment, margin-band noise detection, and figure-inner-text heuristics.
- Modify: `tests/test_ocr_document.py`
- Owns unit tests for zone inference and body-start behavior.
- Modify: `tests/test_ocr_real_paper_regressions.py`
- Owns production-path replay checks on DW, K7/A8, and a control paper.
- Modify: `tests/fixtures/ocr_real_papers/DWQQK2YB/expectations.json`
- Update only if the first-surviving-page behavior is visually verified and the old expectations are now stale.
- Modify: `PROJECT-MANAGEMENT.md`
- Record the completed fix and remaining residuals immediately after verification.
- Modify: `project/current/ocr-v2-remaining-issues-2026-06-18.md`
- Rewrite the residual picture after the measured rerun.
---
### Task 1: Lock First-Surviving-Page Behavior With Red Tests
**Files:**
- Modify: `tests/test_ocr_document.py`
- Modify: `tests/test_ocr_real_paper_regressions.py`
- [ ] **Step 1: Write the failing unit test for first-surviving-page frontmatter retention**
Add this test near the other zone/document normalization tests in `tests/test_ocr_document.py`:
```python
def test_infer_zones_treats_first_surviving_page_as_frontmatter_origin() -> None:
from paperforge.worker.ocr_document import infer_zones
blocks = [
{
"block_id": "p2_title",
"page": 2,
"text": "Real Article Title",
"seed_role": "paper_title",
"role": "paper_title",
"bbox": [80, 120, 900, 180],
"page_width": 1200,
"page_height": 1600,
"marker_signature": {"type": "none"},
},
{
"block_id": "p2_authors",
"page": 2,
"text": "A. Author, B. Author",
"seed_role": "authors",
"role": "authors",
"bbox": [80, 220, 920, 280],
"page_width": 1200,
"page_height": 1600,
"marker_signature": {"type": "none"},
},
{
"block_id": "p2_body",
"page": 2,
"text": "This is the first real body paragraph with enough words to trigger body flow on the first surviving page.",
"seed_role": "body_paragraph",
"role": "body_paragraph",
"bbox": [80, 520, 1030, 650],
"page_width": 1200,
"page_height": 1600,
"marker_signature": {"type": "none"},
},
]
zones = infer_zones(blocks, body_anchor={"status": "ACCEPT"}, reference_anchor={"status": "HOLD"})
assert "p2_title" in zones["frontmatter_main_zone"]["block_ids"]
assert "p2_authors" in zones["frontmatter_main_zone"]["block_ids"]
assert "p2_body" in zones["body_zone"]["block_ids"]
```
- [ ] **Step 2: Write the failing unit test that body can start on the first surviving page**
Add this immediately after the previous test in `tests/test_ocr_document.py`:
```python
def test_infer_zones_allows_body_blocks_on_first_surviving_page() -> None:
from paperforge.worker.ocr_document import infer_zones
blocks = [
{
"block_id": "p2_title",
"page": 2,
"text": "Real Article Title",
"seed_role": "paper_title",
"role": "paper_title",
"bbox": [80, 120, 900, 180],
"page_width": 1200,
"page_height": 1600,
"marker_signature": {"type": "none"},
},
{
"block_id": "p2_heading",
"page": 2,
"text": "Highlights",
"seed_role": "structured_insert",
"role": "structured_insert",
"bbox": [80, 980, 260, 1020],
"page_width": 1200,
"page_height": 1600,
"marker_signature": {"type": "short_fragment"},
},
{
"block_id": "p2_b1",
"page": 2,
"text": "Body continuation should remain eligible for body_zone when it begins on the first surviving page rather than literal page one.",
"seed_role": "body_paragraph",
"role": "body_paragraph",
"bbox": [100, 1060, 1020, 1180],
"page_width": 1200,
"page_height": 1600,
"marker_signature": {"type": "none"},
},
]
zones = infer_zones(blocks, body_anchor={"status": "ACCEPT"}, reference_anchor={"status": "HOLD"})
assert "p2_b1" in zones["body_zone"]["block_ids"]
assert "p2_b1" not in zones["frontmatter_main_zone"]["block_ids"]
```
- [ ] **Step 3: Write the failing real-paper regression for DWQQK2YB first-surviving-page recovery**
Add this in `tests/test_ocr_real_paper_regressions.py` near the other DW regression tests:
```python
def test_dwqqk2yb_first_surviving_page_keeps_title_and_authors(tmp_path: Path) -> None:
result = replay_production_pipeline("DWQQK2YB", tmp_path)
page2_blocks = [b for b in result["structured_blocks"] if int(b.get("page", 0) or 0) == 2]
title_block = next(
b for b in page2_blocks if "Magnetoresponsive Stem Cell Spheroid" in str(b.get("text") or "")
)
authors_block = next(
b for b in page2_blocks if "Ami Yoo" in str(b.get("text") or "") or "Ami Yoo" in str(b.get("block_content") or "")
)
assert title_block.get("role") == "paper_title"
assert authors_block.get("role") in {"authors", "frontmatter_support"}
assert title_block.get("zone") == "frontmatter_main_zone"
assert authors_block.get("zone") == "frontmatter_main_zone"
```
- [ ] **Step 4: Run the focused tests to verify red**
Run: `python -m pytest tests/test_ocr_document.py -k "first_surviving_page" -v --tb=short`
Expected: FAIL because `infer_zones()` still hardcodes literal page 1 and excludes first-surviving-page body blocks.
Run: `python -m pytest tests/test_ocr_real_paper_regressions.py -k "dwqqk2yb_first_surviving_page" -v --tb=short`
Expected: FAIL because DW page 2 title/authors currently fall into `unknown_structural` / `body_zone` fallback.
- [ ] **Step 5: Commit the failing tests only**
```bash
git add tests/test_ocr_document.py tests/test_ocr_real_paper_regressions.py
git commit -m "test: lock first surviving page OCR regressions"
```
---
### Task 2: Implement First-Surviving-Page Zone Inference
**Files:**
- Modify: `paperforge/worker/ocr_document.py`
- Test: `tests/test_ocr_document.py`
- Test: `tests/test_ocr_real_paper_regressions.py`
- [ ] **Step 1: Add a helper that returns the first surviving page**
In `paperforge/worker/ocr_document.py`, near the other zone helpers above `infer_zones()`, add:
```python
def _first_surviving_page(blocks: list[dict]) -> int | None:
pages = sorted({int(block.get("page", 0) or 0) for block in blocks if int(block.get("page", 0) or 0) > 0})
return pages[0] if pages else None
```
- [ ] **Step 2: Generalize `_is_page1_body_start()` without broadening it**
Replace the existing helper with a first-surviving-page-aware version in `paperforge/worker/ocr_document.py`:
```python
def _is_first_page_body_start(block: dict, *, seen_title_or_author: bool) -> bool:
if not seen_title_or_author:
return False
role = block.get("role") or block.get("seed_role")
if role == "unassigned":
role = block.get("seed_role")
text = str(block.get("text") or "").strip()
words = text.split()
marker_type = ((block.get("marker_signature") or {}).get("type") or "none")
if marker_type == "preproof_marker":
return False
if role in {"section_heading", "subsection_heading", "structured_insert", "structured_insert_candidate"}:
return True
if role == "body_paragraph" and len(words) >= 20:
lower = text.lower()
if not lower.startswith(("correspondence", "received", "accepted", "published", "doi")):
return True
return False
```
- [ ] **Step 3: Make `infer_zones()` use the first surviving page instead of literal page 1**
In `infer_zones()` update the frontmatter/body-start block collection from:
```python
page1_candidates = [
b for b in blocks
if int(b.get("page", 0) or 0) == 1
...
]
```
to:
```python
first_surviving_page = _first_surviving_page(blocks)
page1_candidates = [
b for b in blocks
if first_surviving_page is not None
and int(b.get("page", 0) or 0) == first_surviving_page
and ((b.get("marker_signature") or {}).get("type") or "none") != "preproof_marker"
and not _is_reference_item_candidate(b)
and b.get("block_id") is not None
]
```
Also update the body-start call site from `_is_page1_body_start(...)` to `_is_first_page_body_start(...)`.
- [ ] **Step 4: Allow body eligibility on the first surviving page**
In `infer_zones()` replace the body-page gate:
```python
and int(block.get("page", 0) or 0) > 1
```
with:
```python
and first_surviving_page is not None
and (
int(block.get("page", 0) or 0) > first_surviving_page
or (
int(block.get("page", 0) or 0) == first_surviving_page
and _artifact_block_id(block, duplicate_block_ids) not in frontmatter_main_id_set
and _artifact_block_id(block, duplicate_block_ids) not in frontmatter_side_id_set
)
)
```
Keep the existing reference-heading split, reference exclusion, and side-zone exclusion intact.
- [ ] **Step 5: Update the page-band metadata to reflect the surviving first page**
In the returned zone payload, replace:
```python
boundary_band=_page_band(1, 1) if frontmatter_main_ids else None,
```
with:
```python
boundary_band=_page_band(first_surviving_page, first_surviving_page) if frontmatter_main_ids else None,
```
- [ ] **Step 6: Run the focused tests to verify green**
Run: `python -m pytest tests/test_ocr_document.py -k "first_surviving_page" -v --tb=short`
Expected: PASS.
Run: `python -m pytest tests/test_ocr_real_paper_regressions.py -k "dwqqk2yb_first_surviving_page" -v --tb=short`
Expected: PASS.
- [ ] **Step 7: Commit**
```bash
git add paperforge/worker/ocr_document.py tests/test_ocr_document.py tests/test_ocr_real_paper_regressions.py
git commit -m "fix: infer OCR zones from first surviving page"
```
---
### Task 3: Lock And Implement Layout-First Margin-Band Noise And Figure-Inner-Text Recovery
**Files:**
- Modify: `tests/test_ocr_document.py`
- Modify: `paperforge/worker/ocr_roles.py`
- Modify: `tests/test_ocr_real_paper_regressions.py`
- [ ] **Step 1: Write the failing unit test for edge-band watermark noise**
Add this test to `tests/test_ocr_document.py`:
```python
def test_assign_block_role_marks_margin_band_as_noise() -> None:
from paperforge.worker.ocr_roles import assign_block_role
block = {
"page": 4,
"block_id": "wm",
"raw_label": "aside_text",
"block_label": "aside_text",
"text": "Downloaded from https://advanced.onlinelibrary.wiley.com by Example Library. For personal use only.",
"bbox": [1155, 30, 1171, 1535],
"block_bbox": [1155, 30, 1171, 1535],
"page_width": 1224,
"page_height": 1584,
}
assignment = assign_block_role(block, page_blocks=[block])
assert assignment.role == "noise"
assert assignment.confidence >= 0.95
```
- [ ] **Step 2: Write the failing unit test for figure-inner-text recovery**
Add this next to the previous test in `tests/test_ocr_document.py`:
```python
def test_assign_block_role_marks_short_figure_adjacent_label_as_inner_text() -> None:
from paperforge.worker.ocr_roles import assign_block_role
image_block = {
"page": 1,
"block_id": "img1",
"raw_label": "image",
"block_label": "image",
"bbox": [300, 300, 800, 900],
"block_bbox": [300, 300, 800, 900],
"page_width": 1200,
"page_height": 1600,
}
label_block = {
"page": 1,
"block_id": "lab1",
"raw_label": "text",
"block_label": "text",
"text": "Day 7",
"bbox": [320, 320, 410, 360],
"block_bbox": [320, 320, 410, 360],
"page_width": 1200,
"page_height": 1600,
}
assignment = assign_block_role(label_block, page_blocks=[image_block, label_block])
assert assignment.role == "figure_inner_text"
```
- [ ] **Step 3: Write the failing real-paper regression for publisher strips**
Add this to `tests/test_ocr_real_paper_regressions.py`:
```python
def test_k7r8pekw_margin_band_publishers_stay_noise(tmp_path: Path) -> None:
result = replay_production_pipeline("K7R8PEKW", tmp_path)
watermark_blocks = [
b for b in result["structured_blocks"]
if "Downloaded from" in str(b.get("text") or "")
]
assert watermark_blocks, "Expected at least one publisher watermark block"
assert all(b.get("role") == "noise" for b in watermark_blocks)
```
- [ ] **Step 4: Run the focused tests to verify red**
Run: `python -m pytest tests/test_ocr_document.py -k "margin_band or inner_text" -v --tb=short`
Expected: FAIL until the geometry helpers are tightened.
Run: `python -m pytest tests/test_ocr_real_paper_regressions.py -k "k7r8pekw_margin_band" -v --tb=short`
Expected: FAIL because the watermark strips still survive as `unknown_structural` in the replay path.
- [ ] **Step 5: Tighten the geometry-first watermark rule**
In `paperforge/worker/ocr_roles.py`, keep the existing helpers but make the edge-band path slightly stricter and optionally text-confirmed:
```python
def _has_confirmatory_watermark_text(text: str) -> bool:
lower = text.lower()
return "downloaded from" in lower or "for personal use only" in lower
```
Then update `_looks_like_margin_band_noise()` so it still accepts the current extreme geometry but tolerates the real K7/A8 right-edge strips:
```python
very_tall = height >= page_height * 0.30
very_narrow = width <= page_width * 0.12
edge_band = at_left_edge or at_right_edge
text = str(block.get("text") or block.get("block_content") or "")
return edge_band and very_tall and (very_narrow or _has_confirmatory_watermark_text(text))
```
Do not call the helper outside the geometry gate.
- [ ] **Step 6: Broaden figure-inner-text recovery by geometry, not caption text**
In `paperforge/worker/ocr_roles.py`, update `_looks_like_figure_inner_label()` from the current alpha-only filter to allow compact mixed alnum labels while staying short and figure-adjacent:
```python
if not t or len(t) > 12:
return False
if _has_figure_prefix(t):
return False
token_count = len(t.split())
if token_count > 2:
return False
has_alnum = any(ch.isalnum() for ch in t)
if not has_alnum:
return False
```
Keep the existing media adjacency check intact.
- [ ] **Step 7: Run the focused tests to verify green**
Run: `python -m pytest tests/test_ocr_document.py -k "margin_band or inner_text" -v --tb=short`
Expected: PASS.
Run: `python -m pytest tests/test_ocr_real_paper_regressions.py -k "k7r8pekw_margin_band" -v --tb=short`
Expected: PASS.
- [ ] **Step 8: Commit**
```bash
git add paperforge/worker/ocr_roles.py tests/test_ocr_document.py tests/test_ocr_real_paper_regressions.py
git commit -m "fix: tighten OCR margin-band noise and figure inner text"
```
---
### Task 4: Reconcile Verified Expectations And Project Trackers
**Files:**
- Modify: `tests/fixtures/ocr_real_papers/DWQQK2YB/expectations.json`
- Modify: `PROJECT-MANAGEMENT.md`
- Modify: `project/current/ocr-v2-remaining-issues-2026-06-18.md`
- Test: `tests/test_ocr_real_paper_regressions.py`
- [ ] **Step 1: Re-run the targeted real-paper replay checks**
Run: `python -m pytest tests/test_ocr_real_paper_regressions.py -k "DWQQK2YB or K7R8PEKW or CAQNW9Q2" -v --tb=short`
Expected: PASS for the newly added DW/K7 checks, and no regression on the existing CAQ production-path checks.
- [ ] **Step 2: Update DW expectations only if the behavior is now visually correct**
In `tests/fixtures/ocr_real_papers/DWQQK2YB/expectations.json`, change the stale page-2 assertions from the current bug notes:
```json
{"text_contains": "Magnetoresponsive Stem Cell Spheroid", "expected_role": "paper_title", "expected_zone": "frontmatter_main_zone"}
```
and:
```json
{"text_contains": "Ami Yoo", "expected_role": "authors", "expected_zone": "frontmatter_main_zone"}
```
If the replay still produces `frontmatter_support` for the author block but the zone is correct, update the expected role to `frontmatter_support` instead of forcing `authors`.
- [ ] **Step 3: Run the paper-specific regression to verify expectations**
Run: `python -m pytest tests/test_ocr_real_paper_regressions.py -k "DWQQK2YB" -v --tb=short`
Expected: PASS.
- [ ] **Step 4: Update the project tracker immediately after the verified fix**
In `PROJECT-MANAGEMENT.md`, append a new session entry in the existing format:
```md
### 10.x First-surviving-page and edge-band close-out (2026-06-18)
- Problem: preproof-drop papers still used literal page 1 assumptions; repeated publisher strips survived as unknown_structural.
- Root cause: `infer_zones()` anchored frontmatter/body split to `page == 1`; margin-band noise helpers were geometry-correct for only the narrowest cases.
- Fix: switched zone inference to the first surviving page, allowed body flow on that page after the local body-start anchor, and tightened geometry-first margin-band noise detection with optional confirmatory text.
- Result: DW first surviving page frontmatter recovered; K7 watermark strips demoted to noise without broad text-first routing.
- Test status: [insert exact focused pytest commands and result counts from this run].
```
- [ ] **Step 5: Rewrite the remaining-issues note from measured results**
In `project/current/ocr-v2-remaining-issues-2026-06-18.md`, update only the sections that changed:
- mark Issue 1 resolved if DW replay now passes,
- reduce or remove the publisher-noise bucket if the K7/A8 geometry fix closes it,
- leave figure-group and CLI rebuild items deferred,
- do not claim success for any bucket that was not re-measured.
- [ ] **Step 6: Run the final close-out test slice**
Run: `python -m pytest tests/test_ocr_document.py tests/test_ocr_real_paper_regressions.py -k "first_surviving_page or margin_band or inner_text or DWQQK2YB or K7R8PEKW or CAQNW9Q2" -v --tb=short`
Expected: all selected tests PASS.
- [ ] **Step 7: Commit**
```bash
git add tests/fixtures/ocr_real_papers/DWQQK2YB/expectations.json PROJECT-MANAGEMENT.md project/current/ocr-v2-remaining-issues-2026-06-18.md tests/test_ocr_document.py tests/test_ocr_real_paper_regressions.py
git commit -m "docs: record OCR layout close-out progress"
```

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# OCR-v2 Readiness Master Plan Implementation Plan
> **For agentic workers:** REQUIRED SUB-SKILL: Use superpowers:subagent-driven-development (recommended) or superpowers:executing-plans to implement this plan task-by-task. Steps use checkbox (`- [ ]`) syntax for tracking.
**Goal:** Complete the first four OCR-v2 readiness gates so the branch can be called "state healthy" on known layout classes, then leave unseen-paper blind audit as the next-stage gate with explicit entry criteria.
**Architecture:** Keep the anchor-first OCR-v2 backbone and sequence the remaining work as four readiness phases: completeness detection, figure ownership generalization, ordering/boundary authority cleanup, and layout-class coverage formalization. Reuse the existing completeness and figure-group designs where they are already strong, add only the missing execution detail for ordering authority and coverage formalization, and keep project truth files aligned after each phase so the branch has one active narrative.
**Tech Stack:** Python 3.14, existing `paperforge.worker` OCR pipeline, pytest, audit-backed real-paper regressions, repo-local docs under `project/current/`, audit artifacts under `audit/`.
---
## File Structure
- Modify: `paperforge/worker/ocr_blocks.py`
- Owns structured-block build flow; Gate 1 belongs here.
- Modify: `paperforge/worker/ocr_pdf_spans.py`
- Owns PDF-region lookup / backfill utilities; Gate 1 should reuse this rather than fork a second PDF-text path.
- Modify: `paperforge/worker/ocr_health.py`
- Best home for rendered-text coverage summary output.
- Modify: `paperforge/worker/ocr_figures.py`
- Owns Gate 2 figure ownership logic.
- Modify: `paperforge/worker/ocr_document.py`
- Owns Gate 3 boundary authority and normalized structure truth.
- Modify: `paperforge/worker/ocr_orchestrator.py`
- May need small cleanup if ordering authority still leaks downstream.
- Modify: `paperforge/worker/ocr_render.py`
- Keep only minimal presentation ordering after Gate 3.
- Modify: `tests/test_ocr_document.py`
- Gate 1 and Gate 3 unit/regression coverage.
- Modify: `tests/test_ocr_figures.py`
- Gate 2 deterministic inventory coverage.
- Modify: `tests/test_ocr_real_paper_regressions.py`
- Production-path replay checks for Gates 1-3.
- Modify: `tests/test_ocr_real_paper_audit_contracts.py`
- Gate 4 contract tightening; move it off stale fixture-ledger assumptions.
- Create: `audit/coverage_ledger.json`
- Readiness ledger for real audited papers and layout/risk coverage.
- Modify: `project/current/ocr-v2-closeout-priority.md`
- Keep the short active queue aligned after each phase.
- Modify: `project/current/ocr-v2-generalization-boundary.md`
- Keep the broader architectural note aligned.
- Modify: `project/current/ocr-v2-remaining-issues-2026-06-18.md`
- Keep active residuals aligned to actual gates.
- Modify: `PROJECT-MANAGEMENT.md`
- Record each completed phase and any newly parked hard cases.
---
### Task 1: Lock The Readiness-Gates Baseline And Replace The Stale Fixture-Ledger Assumption
**Files:**
- Create: `audit/coverage_ledger.json`
- Modify: `tests/test_ocr_real_paper_audit_contracts.py`
- Modify: `tests/test_ocr_real_paper_regressions.py`
- Modify: `project/current/ocr-v2-closeout-priority.md`
- Modify: `project/current/ocr-v2-remaining-issues-2026-06-18.md`
- [ ] **Step 1: Create an audit-side coverage ledger from the real audited paper set**
Create `audit/coverage_ledger.json` with a minimal but valid readiness ledger shape:
```json
{
"audit_root": "audit",
"papers": [
{"paper_key": "2GN9LMCW", "layout_tags": ["single_column", "special_structure"], "risk_tags": ["special_structure"]},
{"paper_key": "6FGDBFQN", "layout_tags": ["multi_column", "side_caption"], "risk_tags": ["figure_heavy"]},
{"paper_key": "A8E7SRVS", "layout_tags": ["multi_column"], "risk_tags": ["table_heavy"]},
{"paper_key": "CAQNW9Q2", "layout_tags": ["multi_column", "same_page_ref_body_split"], "risk_tags": ["reference_boundary_sensitive", "frontmatter_sensitive"]},
{"paper_key": "DWQQK2YB", "layout_tags": ["multi_column", "preproof_frontmatter", "post_reference_biography"], "risk_tags": ["frontmatter_sensitive", "figure_heavy", "cross_page_caption"]},
{"paper_key": "K7R8PEKW", "layout_tags": ["single_column"], "risk_tags": ["frontmatter_sensitive"]},
{"paper_key": "SAN9AYVR", "layout_tags": ["multi_column"], "risk_tags": ["special_structure"]},
{"paper_key": "TSCKAVIS", "layout_tags": ["single_column", "review_callout"], "risk_tags": ["special_structure", "table_heavy"]}
]
}
```
- [ ] **Step 2: Move the audit-contract test off `tests/fixtures` manifest lookup**
In `tests/test_ocr_real_paper_audit_contracts.py`, change the ledger root and replace the current coarse check with an explicit readiness-class check:
```python
LEDGER_PATH = Path(__file__).resolve().parents[1] / "audit" / "coverage_ledger.json"
def test_gold_set_covers_readiness_layout_classes() -> None:
manifest = _load_manifest()
all_layouts = {tag for paper in manifest["papers"] for tag in paper.get("layout_tags", [])}
assert "preproof_frontmatter" in all_layouts
assert "same_page_ref_body_split" in all_layouts
assert any(tag in all_layouts for tag in {"side_caption", "multi_panel"})
assert "post_reference_biography" in all_layouts
assert any(tag in all_layouts for tag in {"review_callout", "special_structure"})
```
- [ ] **Step 3: Add a Gate 2 red regression that no longer allows ambiguity as success for DW Figure 3**
In `tests/test_ocr_real_paper_regressions.py`, replace the current permissive assertion with a strict ownership target:
```python
def test_dwqqk2yb_figure3_is_fully_owned_not_merely_captured(tmp_path: Path) -> None:
result = replay_production_pipeline("DWQQK2YB", tmp_path)
reader_payload = result["reader_payload"]
matched, ambiguous = _reader_figure_index(reader_payload)
assert 3 in matched, "Fig 3 should be matched, not left ambiguous"
assert 3 not in ambiguous, "Fig 3 ambiguity is no longer acceptable after Gate 2"
```
- [ ] **Step 4: Run the red baseline checks**
Run: `python -m pytest tests/test_ocr_real_paper_audit_contracts.py -v`
Run: `python -m pytest tests/test_ocr_real_paper_regressions.py -k "dwqqk2yb_figure3_is_fully_owned" -v`
Expected: audit-ledger tests PASS once the ledger exists; the Gate 2 DW ownership test FAILS until Task 3 lands.
- [ ] **Step 5: Commit the baseline and ledger restoration**
```bash
git add audit/coverage_ledger.json tests/test_ocr_real_paper_audit_contracts.py tests/test_ocr_real_paper_regressions.py project/current/ocr-v2-closeout-priority.md project/current/ocr-v2-remaining-issues-2026-06-18.md
git commit -m "test: lock OCR readiness baseline and restore coverage ledger"
```
---
### Task 2: Implement Gate 1 Completeness Signals
**Files:**
- Modify: `paperforge/worker/ocr_blocks.py`
- Modify: `paperforge/worker/ocr_pdf_spans.py`
- Modify: `paperforge/worker/ocr_health.py`
- Modify: `tests/test_ocr_document.py`
- Modify: `tests/test_ocr_real_paper_regressions.py`
- [ ] **Step 1: Add failing page-level and region-level completeness tests**
In `tests/test_ocr_document.py`, add two focused tests:
```python
def test_page_text_coverage_flags_low_ratio_when_pdf_text_dominates() -> None:
from paperforge.worker.ocr_blocks import _summarize_page_text_coverage
result = _summarize_page_text_coverage(
ocr_text="short text",
pdf_text="short text plus a much longer native PDF segment that should dominate coverage",
)
assert result["page_text_coverage_status"] == "low"
assert result["page_text_coverage_ratio_chars"] < 0.5
def test_region_text_completeness_marks_empty_vs_pdf() -> None:
from paperforge.worker.ocr_blocks import _classify_region_text_completeness
result = _classify_region_text_completeness(
ocr_text="",
pdf_region_text="A complete sentence present in the PDF native text layer.",
)
assert result["text_completeness_status"] == "empty_vs_pdf"
```
- [ ] **Step 2: Add failing rendered-gap audit test**
Still in `tests/test_ocr_document.py`, add:
```python
def test_rendered_text_coverage_flags_missing_pdf_segment() -> None:
from paperforge.worker.ocr_health import audit_rendered_text_coverage
result = audit_rendered_text_coverage(
rendered_markdown="Only the introduction survived.",
pdf_segments=["Only the introduction survived.", "A long methods segment that is missing from render."],
)
assert result["rendered_text_gap_count"] == 1
```
- [ ] **Step 3: Run red tests for Gate 1**
Run: `python -m pytest tests/test_ocr_document.py -k "coverage or completeness or rendered_text_coverage" -v`
Expected: FAIL.
- [ ] **Step 4: Add the minimal completeness helpers and wire them into structured-block build**
In `paperforge/worker/ocr_blocks.py`, add small helper seams and attach the emitted metadata before `normalize_document_structure()`:
```python
def _summarize_page_text_coverage(*, ocr_text: str, pdf_text: str) -> dict:
ocr_chars = len((ocr_text or "").strip())
pdf_chars = len((pdf_text or "").strip())
if pdf_chars == 0:
return {"page_text_coverage_status": "missing_pdf_text", "page_text_coverage_ratio_chars": 1.0}
ratio = ocr_chars / max(pdf_chars, 1)
return {
"page_text_coverage_status": "low" if ratio < 0.6 else "ok",
"page_text_coverage_ratio_chars": ratio,
}
def _classify_region_text_completeness(*, ocr_text: str, pdf_region_text: str) -> dict:
ocr = (ocr_text or "").strip()
pdf = (pdf_region_text or "").strip()
if not pdf:
return {"text_completeness_status": "pdf_unavailable", "text_completeness_confidence": 0.0}
if not ocr:
return {"text_completeness_status": "empty_vs_pdf", "text_completeness_confidence": 0.95}
if len(ocr) < len(pdf) * 0.45:
return {"text_completeness_status": "short_vs_pdf", "text_completeness_confidence": 0.8}
if pdf.startswith(ocr) and len(pdf) > len(ocr) + 24:
return {"text_completeness_status": "likely_missing_tail", "text_completeness_confidence": 0.85}
return {"text_completeness_status": "complete", "text_completeness_confidence": 0.7}
```
- [ ] **Step 5: Add rendered-gap audit in health layer**
In `paperforge/worker/ocr_health.py`, add:
```python
def audit_rendered_text_coverage(*, rendered_markdown: str, pdf_segments: list[str]) -> dict:
missing = [segment for segment in pdf_segments if segment and segment not in rendered_markdown]
return {
"rendered_text_gap_count": len(missing),
"rendered_text_gap_examples": missing[:3],
}
```
- [ ] **Step 6: Run Gate 1 focused tests until green**
Run: `python -m pytest tests/test_ocr_document.py -k "coverage or completeness or rendered_text_coverage" -v`
Expected: PASS.
- [ ] **Step 7: Run the nearest production-path Gate 1 regression slice**
Run: `python -m pytest tests/test_ocr_real_paper_regressions.py -k "DWQQK2YB or CAQNW9Q2" -v`
Expected: PASS or only pre-existing skipped tests; no new failures.
- [ ] **Step 8: Commit Gate 1**
```bash
git add paperforge/worker/ocr_blocks.py paperforge/worker/ocr_pdf_spans.py paperforge/worker/ocr_health.py tests/test_ocr_document.py tests/test_ocr_real_paper_regressions.py PROJECT-MANAGEMENT.md
git commit -m "feat: add OCR completeness coverage signals"
```
---
### Task 3: Implement Gate 2 Group-First Figure Ownership Generalization
**Files:**
- Modify: `paperforge/worker/ocr_figures.py`
- Modify: `paperforge/worker/ocr_health.py`
- Modify: `tests/test_ocr_figures.py`
- Modify: `tests/test_ocr_real_paper_regressions.py`
- [ ] **Step 1: Add the failing deterministic group-first unit test if it is not already present**
In `tests/test_ocr_figures.py`, keep the unit explicit:
```python
def test_group_first_matching_prefers_same_row_pair_over_single_asset() -> None:
from paperforge.worker.ocr_figures import build_figure_inventory
blocks = [
{"block_id": 1, "role": "figure_caption", "text": "Fig. 2 A and B, MRI and gross anatomic correlation.", "page": 3, "bbox": [80, 120, 420, 210]},
{"block_id": 2, "role": "media_asset", "raw_label": "image", "page": 3, "bbox": [450, 120, 780, 520]},
{"block_id": 3, "role": "media_asset", "raw_label": "image", "page": 3, "bbox": [805, 120, 1130, 520]},
]
inventory = build_figure_inventory(blocks, page_width=1200)
matched = inventory["matched_figures"]
assert len(matched) == 1
assert [a["block_id"] for a in matched[0]["matched_assets"]] == [2, 3]
```
- [ ] **Step 2: Run the focused Gate 2 red tests**
Run: `python -m pytest tests/test_ocr_figures.py -k "group_first_matching_prefers_same_row_pair or partition_assets_by_caption_bands" -v`
Run: `python -m pytest tests/test_ocr_real_paper_regressions.py -k "dwqqk2yb_figure3_is_fully_owned" -v`
Expected: at least the DW ownership test FAILS until the refactor lands.
- [ ] **Step 3: Implement candidate-group-first matching in `ocr_figures.py`**
Add deterministic candidate group helpers and reserve asset sets rather than individual assets:
```python
def _candidate_group_entry(group_id: str, page: int, media_blocks: list[dict], group_type: str, evidence: list[str]) -> dict:
return {
"group_id": group_id,
"page": page,
"group_type": group_type,
"asset_block_ids": [b.get("block_id") for b in media_blocks],
"media_blocks": media_blocks,
"group_evidence": evidence,
}
def _build_candidate_figure_groups_from_assets(assets: list[dict]) -> list[dict]:
groups: list[dict] = []
for asset in assets:
groups.append(_candidate_group_entry(f"single_{asset['block_id']}", asset.get("page", 0), [asset], "single_asset", ["single_asset"]))
return groups
```
Then update `build_figure_inventory()` so legend scoring consumes candidate groups first, and fallback only sees truly unclaimed assets.
- [ ] **Step 4: Expose grouped-vs-single counters in health output**
In `paperforge/worker/ocr_health.py`, add grouped counters to the figure summary:
```python
figure_summary["grouped_match_count"] = sum(1 for item in matched_figures if len(item.get("matched_assets", [])) > 1)
figure_summary["single_match_count"] = sum(1 for item in matched_figures if len(item.get("matched_assets", [])) == 1)
```
- [ ] **Step 5: Run Gate 2 tests until green**
Run: `python -m pytest tests/test_ocr_figures.py -k "group_first_matching_prefers_same_row_pair or partition_assets_by_caption_bands" -v`
Run: `python -m pytest tests/test_ocr_real_paper_regressions.py -k "DWQQK2YB" -v`
Expected: PASS, with no ambiguity-acceptable DW Figure 3 path remaining.
- [ ] **Step 6: Commit Gate 2**
```bash
git add paperforge/worker/ocr_figures.py paperforge/worker/ocr_health.py tests/test_ocr_figures.py tests/test_ocr_real_paper_regressions.py PROJECT-MANAGEMENT.md
git commit -m "feat: generalize OCR figure ownership with group-first matching"
```
---
### Task 4: Implement Gate 3 Ordering And Boundary Authority Cleanup
**Files:**
- Modify: `paperforge/worker/ocr_document.py`
- Modify: `paperforge/worker/ocr_orchestrator.py`
- Modify: `paperforge/worker/ocr_render.py`
- Modify: `tests/test_ocr_document.py`
- Modify: `tests/test_ocr_real_paper_regressions.py`
- [ ] **Step 1: Add a failing same-page mixed-layout authority test**
In `tests/test_ocr_document.py`, add:
```python
def test_same_page_reference_boundary_is_resolved_upstream_not_in_renderer() -> None:
from paperforge.worker.ocr_document import normalize_document_structure
blocks = [
{"block_id": "body_1", "page": 7, "seed_role": "body_paragraph", "text": "Conclusion text above refs.", "bbox": [80, 420, 980, 510]},
{"block_id": "refs_h", "page": 7, "seed_role": "reference_heading", "text": "References", "bbox": [80, 900, 320, 960]},
{"block_id": "ref_1", "page": 7, "seed_role": "reference_item", "text": "[1] First ref", "bbox": [80, 980, 980, 1060]},
]
_doc, normalized = normalize_document_structure(blocks)
by_id = {b["block_id"]: b for b in normalized}
assert by_id["body_1"]["role"] == "body_paragraph"
assert by_id["ref_1"]["role"] == "reference_item"
```
- [ ] **Step 2: Run the red Gate 3 authority tests**
Run: `python -m pytest tests/test_ocr_document.py -k "reference_boundary_is_resolved_upstream" -v`
Expected: FAIL if renderer/orchestrator still carries meaningful authority for this case.
- [ ] **Step 3: Move authority to explicit structure artifacts**
In `paperforge/worker/ocr_document.py`, add a small artifact-first helper:
```python
def _same_page_reference_boundary_y(page_blocks: list[dict]) -> float | None:
headings = [b for b in page_blocks if b.get("seed_role") == "reference_heading"]
if not headings:
return None
return min((b.get("bbox") or [0, 0, 0, 0])[1] for b in headings)
```
Then classify blocks above that Y boundary as body-capable and blocks below it as reference-capable before renderer ordering runs.
- [ ] **Step 4: Shrink renderer-side reorder logic**
In `paperforge/worker/ocr_render.py`, replace any correctness-bearing mixed-page reorder branch with a no-op or assertion that structure ordering already exists:
```python
# ponytail: renderer should consume normalized order, not invent it.
ordered_blocks = structured_blocks
```
If a minimal presentational sort is still required, keep it local to already-normalized sibling groups only.
- [ ] **Step 5: Run Gate 3 focused tests until green**
Run: `python -m pytest tests/test_ocr_document.py -k "reference_boundary_is_resolved_upstream or same_page" -v`
Run: `python -m pytest tests/test_ocr_real_paper_regressions.py -k "CAQNW9Q2 or DWQQK2YB" -v`
Expected: PASS.
- [ ] **Step 6: Commit Gate 3**
```bash
git add paperforge/worker/ocr_document.py paperforge/worker/ocr_orchestrator.py paperforge/worker/ocr_render.py tests/test_ocr_document.py tests/test_ocr_real_paper_regressions.py PROJECT-MANAGEMENT.md project/current/ocr-v2-generalization-boundary.md
git commit -m "refactor: move OCR ordering authority upstream"
```
---
### Task 5: Implement Gate 4 Layout-Coverage Formalization
**Files:**
- Modify: `audit/coverage_ledger.json`
- Modify: `tests/test_ocr_real_paper_audit_contracts.py`
- Modify: `project/current/ocr-v2-generalization-boundary.md`
- Modify: `project/current/ocr-v2-closeout-priority.md`
- Modify: `PROJECT-MANAGEMENT.md`
- [ ] **Step 1: Expand the audit ledger taxonomy beyond broad layout labels**
In `audit/coverage_ledger.json`, keep the new readiness-class tags and normalize naming to the approved set:
```json
"layout_tags": ["multi_column", "same_page_ref_body_split", "preproof_frontmatter"]
```
Use only these readiness-class names in this phase:
```text
multi_panel
side_caption
same_page_ref_body_split
post_reference_biography
preproof_frontmatter
review_callout
special_structure
```
- [ ] **Step 2: Tighten the contract tests for page/object coverage by class**
In `tests/test_ocr_real_paper_audit_contracts.py`, add a per-class minimum:
```python
def test_layout_class_manifest_has_named_representatives() -> None:
manifest = _load_manifest()
by_tag = {}
for paper in manifest["papers"]:
for tag in paper.get("layout_tags", []):
by_tag.setdefault(tag, set()).add(paper["paper_key"])
assert by_tag["preproof_frontmatter"]
assert by_tag["same_page_ref_body_split"]
assert by_tag["post_reference_biography"]
assert by_tag["review_callout"] or by_tag["special_structure"]
```
- [ ] **Step 3: Run Gate 4 tests**
Run: `python -m pytest tests/test_ocr_real_paper_audit_contracts.py -v`
Expected: PASS.
- [ ] **Step 4: Update project truth files to say layout coverage is now a tracked capability surface**
Use these exact narrative shifts:
```text
old: examples of useful gold papers
new: explicit readiness classes with named representatives and contract coverage
```
Write the updated summary into:
- `project/current/ocr-v2-generalization-boundary.md`
- `project/current/ocr-v2-closeout-priority.md`
- `PROJECT-MANAGEMENT.md`
- [ ] **Step 5: Commit Gate 4**
```bash
git add audit/coverage_ledger.json tests/test_ocr_real_paper_audit_contracts.py project/current/ocr-v2-generalization-boundary.md project/current/ocr-v2-closeout-priority.md PROJECT-MANAGEMENT.md
git commit -m "test: formalize OCR layout coverage readiness classes"
```
---
### Task 6: Record Gate 5 Blind-Audit Entry Criteria Without Executing It
**Files:**
- Modify: `project/current/ocr-v2-closeout-priority.md`
- Modify: `project/current/ocr-v2-remaining-issues-2026-06-18.md`
- Modify: `PROJECT-MANAGEMENT.md`
- [ ] **Step 1: Add explicit blind-audit trigger wording to the short queue**
Insert this kind of entry in `project/current/ocr-v2-closeout-priority.md` once Gates 1-4 are complete:
```text
Next stage after readiness: run bounded unseen-paper blind audit with no code changes during the audit window.
```
- [ ] **Step 2: Record the gate-entry checklist in `PROJECT-MANAGEMENT.md`**
Add a short checklist block:
```text
- Gates 1-4 complete
- no active P0 trust risk in known fixtures
- project truth files aligned
- blind audit sample selected before execution
```
- [ ] **Step 3: Commit the Gate 5 handoff wording**
```bash
git add project/current/ocr-v2-closeout-priority.md project/current/ocr-v2-remaining-issues-2026-06-18.md PROJECT-MANAGEMENT.md
git commit -m "docs: record OCR blind-audit entry gate"
```
---
## Verification Checklist Before Claiming "State Healthy"
- [ ] `python -m pytest tests/test_ocr_document.py -v`
- [ ] `python -m pytest tests/test_ocr_figures.py -v`
- [ ] `python -m pytest tests/test_ocr_real_paper_regressions.py -v`
- [ ] `python -m pytest tests/test_ocr_real_paper_audit_contracts.py -v`
- [ ] `python -m pytest tests/cli/ tests/unit/ -v --tb=short`
- [ ] `ruff check paperforge/ tests/`
Only after those pass, and only after project truth files point at Gate 5 as the next step, may OCR-v2 be called "state healthy" on known layout classes.

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# OCR-v2 Unified Close-Out Design
> Date: 2026-06-17
> Status: active
> Scope: unify the remaining OCR-v2 close-out work into one bounded execution track instead of splitting it across multiple small plans.
## Goal
Finish the current OCR-v2 close-out pass with one coherent sequence:
1. drop useless preproof cover page 1 at the structured-block layer
2. conservatively tighten tail/post-reference normalization so it stops damaging valid structure
3. run a bounded rebuild and audit pass on the 8 gold papers plus up to 10 additional vault papers selected for layout coverage
4. rewrite the residual error picture from evidence
5. decide whether figure-group work is truly the next dominant blocker
The purpose of this design is not to open another major OCR architecture branch. The purpose is to close the current one cleanly.
## Why This Design Exists
The codebase already passed through the large OCR-v2 redesign: anchor-first parsing, late role resolution, verified-role gate, figure/table inventory, and production-path replay harness.
The remaining work is now smaller in count but easy to scatter:
- one remaining `preproof` page-1 problem
- residual post-reference/tail normalization issues
- some expectations and audits that may now be stale
- unresolved figure ownership work that may or may not actually be the top blocker after another audit pass
Without a unified close-out design, the branch can bounce between small fixes and large architecture ideas without a stable stop condition.
## Non-Goals
This design does not:
- redesign OCR-v2 from scratch
- run full-vault rebuilds indiscriminately
- widen `backmatter_heading` classification through a large phrase list
- add journal-specific figure rescue logic
- force figure-group refactor into the current pass before post-fix audit evidence says it is necessary
## Current Branch Assumptions
The design assumes the following are already true on `ocr-v2`:
- same-page body/reference split has already improved materially
- page-1 correspondence routing now has an explicit `frontmatter_support` path
- tail/post-reference damage is smaller than before but not fully closed
- remaining known issues include:
- preproof cover page 1 noise still entering the document path
- post-reference normalization still having edge-case damage or unnecessary taxonomy pressure
- figure ownership still unresolved on some mixed-layout papers
## Design Overview
The unified close-out pass is intentionally conservative.
It uses the existing pipeline and changes as little surface area as possible:
- preproof cover-page removal happens early so downstream logic never sees useless page-1 cover blocks
- tail/post-reference cleanup prefers "do not damage valid structure" over introducing richer semantic labeling
- audit expansion is bounded to a small, purposeful layout sample instead of the full vault
- figure-group work is demoted from an assumed next step to a decision gate driven by residual evidence
## Section 1: Preproof Page-1 Removal
### Problem
Some preproof papers carry a page-1 cover sheet that is not useful article content. It adds title/author/PII-like clutter that then has to be suppressed, rescued, or routed later.
This is the wrong layer to fight it. If the page is truly useless cover material, the cleanest fix is to remove it before document normalization.
### Decision
When the pipeline can confidently identify a page-1 preproof cover page, it should remove that page from the structured-block stream before `normalize_document_structure()` runs.
### Rationale
- the user explicitly does not want that information preserved
- dropping it early removes downstream contamination in zones, role rescue, and rendering
- this is cleaner than keeping the page and repeatedly teaching later logic to ignore it
### Boundary
This is not a general "drop page 1 if it looks weird" rule.
It applies only when there is strong evidence of a true preproof cover page, using existing preproof signals and page-1 block patterns already available in the pipeline.
### Expected Outcome
- page-1 preproof cover blocks do not appear in `structured_blocks`
- downstream frontmatter suppression logic has less work to do
- the known DW preproof page-1 issue disappears at the source instead of being patched later
## Section 2: Conservative Tail/Post-Reference Cleanup
### Problem
The remaining post-reference issues are mostly not about needing a perfect `backmatter_heading` taxonomy. They are about not harming blocks that should remain stable.
Trying to make `backmatter_heading` more complete without heavy text matching would be brittle. Trying to do it with heavy text matching would create exactly the kind of heuristic bloat the close-out should avoid.
### Decision
Do not expand `backmatter_heading` aggressively.
Instead:
- preserve valid existing `section_heading` / `subsection_heading` / related heading roles when the evidence is already good
- avoid converting valid body or object-adjacent blocks into `backmatter_body`
- keep post-reference normalization focused on preventing structural damage rather than perfecting taxonomy
### Rationale
- fewer text-matching heuristics
- lower regression risk
- aligns with the user's preference not to lean on fragile phrase-based heading detection
- keeps the close-out focused on trust and stability, not label cosmetics
### Expected Outcome
- fewer false `backmatter_body` conversions
- valid headings survive more often as ordinary heading roles
- post-reference structure is cleaner even if the taxonomy remains intentionally boring
## Section 3: Bounded Audit Expansion
### Problem
The next verification pass needs broader evidence than the current 8 gold papers alone, but a full-vault rebuild is unnecessary and expensive.
### Decision
Run the close-out verification pass on:
- the existing 8 gold papers
- up to 10 additional vault papers selected specifically to expand layout coverage
### Selection Method
The extra papers should be selected by layout class, not by randomness or convenience.
Priority layout classes to cover:
1. preproof cover page papers
2. same-page body/reference split papers
3. biography/backmatter-tail papers
4. multi-panel figure papers
5. narrow-caption or side-caption figure papers
6. table-heavy papers
7. publisher margin-noise papers
The chosen set does not need to maximize paper count. It needs to maximize coverage diversity.
### Rationale
- bounded cost
- higher signal than a blind larger run
- gives enough evidence to decide whether figure ownership or some other class is really the next blocker
### Expected Outcome
- the branch gets a broader residual map without opening a full-vault operational loop
- layout coverage becomes more trustworthy for the next prioritization decision
## Section 4: Residual Write-Back
### Problem
The branch has accumulated multiple status documents, plans, and handoff notes. After the bounded rebuild/audit pass, the residual state needs to be rewritten from current evidence so the branch does not drift back into contradictory next steps.
### Decision
After the bounded rebuild/audit pass, update the active project trackers so they reflect the real remaining categories.
At minimum, update:
- `project/current/ocr-error-root-cause-fix-queue.md`
- `PROJECT-MANAGEMENT.md`
### What Must Be Written Back
The write-back should separate:
- issues solved by the current pass
- stale truth / stale expectations revealed by the new audit
- remaining document-structure residuals
- remaining figure-ownership residuals
### Rationale
- keeps the branch's next-step guidance singular
- prevents old June plans from silently retaking control of prioritization
## Section 5: Figure-Group Decision Gate
### Problem
Figure-group work is real, but it is also a larger architectural thread. Reopening it too early would blur whether the current close-out pass actually succeeded.
### Decision
Do not make figure-group refactor an automatic part of this implementation pass.
Instead, turn it into a decision gate after rebuild and audit.
### Gate Condition
Reopen `group-first figure inventory` only if the post-audit residuals show that:
1. figure ownership is now the dominant remaining class
2. it clearly exceeds the remaining document-structure and truth-maintenance residuals
3. the bounded sample confirms it across more than one paper/layout family
### Rationale
- keeps the current pass bounded
- avoids premature architecture expansion
- makes the figure-group decision evidence-led instead of taste-led
## Data Flow Summary
The intended close-out flow becomes:
```text
raw OCR blocks
-> structured block build
-> preproof page-1 drop (when confidently identified)
-> normalize_document_structure()
-> conservative tail/post-reference cleanup
-> render + artifacts
-> rebuild/audit on 8 gold + up to 10 extra vault papers
-> residual rewrite
-> figure-group decision gate
```
## Testing Strategy
The implementation plan derived from this spec should include tests at four levels:
1. **unit tests**
- preproof page-1 removal fires only on confident preproof cover pages
- post-reference cleanup preserves valid heading/body/object structure conservatively
2. **document-pipeline tests**
- `normalize_document_structure()` sees the intended inputs after the preproof drop
- tail/post-reference behavior does not regress in known mixed-page cases
3. **real-paper regression tests**
- the current gold-paper production-path checks remain green or improve
- specific known papers covering preproof and mixed tail behavior are locked
4. **bounded audit verification**
- 8 gold papers plus up to 10 vault papers are rebuilt and inspected through the repo's audit helpers
## Risks And Mitigations
### Risk 1: dropping page 1 too aggressively
Mitigation:
- require strong preproof-cover evidence
- keep the filter narrow and explicit
- lock non-preproof page-1 behavior with regression tests
### Risk 2: conservative cleanup leaves taxonomy imperfect
Mitigation:
- accept this intentionally
- prioritize stability over label refinement in the current pass
- only reopen taxonomy work if it later becomes a genuine blocker
### Risk 3: 10-paper sample misses an important layout family
Mitigation:
- select papers deliberately by layout class
- document which class each chosen sample covers
### Risk 4: figure-group still turns out to be the main blocker
Mitigation:
- this design already includes a decision gate to reopen that work immediately after audit evidence confirms it
## Acceptance Criteria
This unified close-out pass is successful if all of the following are true:
1. preproof cover page 1 is removed at the structured-block layer for the targeted preproof case(s)
2. post-reference/tail cleanup causes fewer false structure conversions without expanding large phrase-matching taxonomies
3. the branch is verified on 8 gold papers plus up to 10 extra vault papers selected for layout coverage
4. project-tracking docs are rewritten to reflect the post-pass residuals from current evidence
5. the branch ends with a clear evidence-based answer to: "is figure-group the next dominant blocker or not?"
## Implementation Handoff
This spec should produce one implementation plan, not several small competing plans.
That plan should be task-based, but unified, with at least these task groups:
1. preproof page-1 structured-layer removal
2. conservative tail/post-reference cleanup
3. bounded rebuild and audit sample selection/execution
4. residual write-back
5. figure-group decision gate

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# DWQ Repair And Completeness Phase Design
> Date: 2026-06-18
> Status: approved design, pending user review
> Scope: clean stale OCR tracking files first, then repair the remaining real DWQQK2YB issues, rebuild derived artifacts, and stage the next completeness-check phase without mixing its implementation into the same risky change set.
## Goal
Run one coherent next phase that stops the branch from inheriting stale issue files, repairs the remaining real DWQQK2YB support-routing and figure-ownership problems, refreshes derived artifacts, and leaves the completeness-check layer fully specified and ready for implementation planning.
## Why This Phase Exists
The branch state after `PROJECT-MANAGEMENT.md` section `9.7` is healthier than several current tracking files imply.
Specifically:
- some issue files still report already-fixed P0-P2 items as open,
- DWQQK2YB still has one real support-routing gap,
- DWQQK2YB still has one real figure-ownership gap,
- completeness checking is the next structural safeguard, but it should not be coded in the same pass as the DW repairs.
## Chosen Approach
Chosen approach: fix the repository's truth surfaces first, then repair the remaining DWQQK2YB issues, then rebuild, and only after that plan the completeness-check implementation.
Execution order:
```text
1. Clean stale issue files and align them with 9.7
2. Repair DWQQK2YB frontmatter support routing
3. Repair DWQQK2YB figure ownership
4. Rebuild derived artifacts for affected papers
5. Write / preserve completeness-check spec and implementation plan as the next phase
```
Why this ordering won:
- stale issue files are currently poisoning the next-step narrative,
- support routing and figure ownership are real unresolved defects on DWQQK2YB,
- rebuild should happen after code repair so the derived assets reflect the fixed logic,
- completeness-check implementation should not be entangled with ownership debugging in the same patch set.
## Design Constraints
1. File cleanup happens first.
2. Only real unresolved issues stay in `project/current/` and `PROJECT-MANAGEMENT.md`.
3. DWQQK2YB support routing must become more layout-first and less page-1-text-special-case driven.
4. DWQQK2YB figure ownership must prefer unresolved over over-merged claims.
5. Rebuild is required after the code fix so derived figure assets and structure artifacts reflect the repaired logic.
6. Completeness check remains part of this phase's planning surface, but not part of the same production-code patch.
## Phase Breakdown
## 1. Truth-File Cleanup First
This phase begins by aligning the project trackers with the actual post-`9.7` branch state.
Files to reconcile first:
- `project/current/ocr-v2-remaining-issues-2026-06-18.md`
- `project/current/ocr-v2-closeout-priority.md`
- `PROJECT-MANAGEMENT.md`
The cleanup should remove or rewrite stale entries such as:
- `media_asset -> body_paragraph` when the latest evidence says it was a stale audit-truth bucket,
- outdated DW biography mismatch wording if the fixture/audit state has already moved,
- outdated P0-P2 residuals that were closed in section `9.7`.
The cleanup should preserve genuine unresolved items:
- DWQQK2YB frontmatter support/equal-contribution routing,
- DWQQK2YB figure ownership,
- any still-real backmatter taxonomy ambiguity,
- completeness-check as the next architectural safeguard.
Reason for doing this first:
- every later planning and implementation step otherwise inherits bad state and mis-prioritizes work.
## 2. DWQQK2YB Frontmatter Support Repair
### Problem
Some first-surviving-page support blocks on DWQQK2YB still fall into `frontmatter_noise` or body-like fallback when they should be preserved as `frontmatter_support`.
Examples include:
- corresponding-author support lines,
- equal-contribution support lines,
- support-like blocks adjacent to the title/author/affiliation cluster.
### Root Cause Direction
The current routing still leans too much on:
- page-1-only cases,
- text-start heuristics,
- raw-label-specific routing.
That leaves gaps for first-surviving-page cases where the blocks are:
- `raw_label=text`, not explicit `footnote`,
- visually embedded in the frontmatter cluster,
- support-like by geometry and style rather than by exact prefix.
### Intended Repair Direction
Promote layout evidence to the primary classifier for frontmatter support:
- first surviving page or first-frontmatter page,
- top-of-page placement,
- `frontmatter_main_zone` or `frontmatter_side_zone`,
- `support_like` style family or similar span/layout evidence,
- adjacency to title/authors/affiliations,
- optional marker help such as superscript / affiliation marker,
- text only as confirmatory evidence, not as the main gate.
Expected result:
- support-like blocks stay in frontmatter flow,
- they no longer leak into body or generic noise,
- they remain isolated from later tail/reference logic.
## 3. DWQQK2YB Figure Ownership Repair
### Problem
DWQQK2YB still fails figure ownership expectations on the mixed post-reference figure pages.
Observed current behavior:
- Figure 2 claims far too many assets,
- Figure 3 has a caption but no owned asset,
- Figure 4 also over-claims assets.
This is not a fulltext problem. It is an ownership/materialization problem in the figure-inventory path.
### Root Cause Direction
The likely failure seam is the candidate-group and asset-claim boundary logic, not caption text detection itself.
The current matching path appears too willing to treat a broad same-page media cluster as one ownership pool.
That creates two failure modes:
1. **over-merge**: one caption claims multiple visual bands or adjacent figure groups,
2. **empty local match**: a caption gets no local candidate group and becomes ambiguous.
### Intended Repair Direction
Ownership should be constrained by local visual partitioning before caption claim:
- partition same-page pre-caption media into smaller vertical/column-aware groups,
- keep caption ownership local to its most plausible band/group,
- do not let a narrow or local caption swallow the whole page's media cluster,
- if no local candidate exists, keep the figure unresolved rather than attaching the wrong assets.
Policy for this phase:
- prefer unresolved over wrong merge,
- fix DWQQK2YB specifically by improving the generic partition/claim logic, not by adding a one-paper rescue rule.
## 4. Rebuild After Repair
Once the code fixes land, rebuild is mandatory for the affected papers.
Why:
- figure JPGs and other derived artifacts are persisted outputs,
- earlier composite/crop/ownership logic changes do not retroactively rewrite already-emitted derived assets,
- rebuild is needed even when the underlying OCR result JSON is unchanged.
Canonical rebuild path for this phase:
- `scripts/dev/ocr_rebuild_paper.py`
This should be used to regenerate:
- structured blocks,
- reader figures,
- inventories,
- render outputs,
- derived asset files.
## 5. Completeness Check Stays In This Phase As Planned Next Work
The completeness-check layer remains part of the phase design, but as the next implementation slice after the DW repairs.
It keeps the already-approved structure:
- page text coverage,
- region coverage,
- fulltext coverage.
It remains:
- fuzzy, not exact-match,
- signal-first,
- inserted after raw-role assignment and before normalization for the early checks,
- render-audit-based for the downstream fulltext check.
This phase should preserve the spec and include it in the next implementation plan, but should not bundle its production-code changes with the DW ownership repair patch unless the user explicitly re-scopes again.
## Verification Plan
This phase must verify four things separately.
### 1. Truth files are no longer stale
- `project/current/*` and `PROJECT-MANAGEMENT.md` agree on what is really fixed vs really open.
### 2. DWQQK2YB support routing is correct
- corresponding-author / equal-contribution blocks on the first surviving page remain in frontmatter support flow,
- they do not leak into body output.
### 3. DWQQK2YB figure ownership is correct
- Figure 2 no longer over-claims,
- Figure 3 either resolves correctly or remains intentionally unresolved with no false claim,
- Figure 4 no longer over-claims,
- ownership regression test reflects the repaired generic logic.
### 4. Rebuilt artifacts reflect the fixed logic
- derived figure assets and reader-figure artifacts are regenerated after the fix,
- rebuild output no longer lags behind the repaired code path.
## Non-Goals
- Do not reopen all figure-group architecture work unless the DW fix proves insufficient.
- Do not mix completeness-check production implementation into the same risky code patch.
- Do not keep stale issues around “for history” in the active current-priority files.
## Deliverables
This combined phase should produce:
1. cleaned current-truth documentation,
2. a DWQQK2YB support-routing repair,
3. a DWQQK2YB figure-ownership repair,
4. a rebuild pass over affected derived artifacts,
5. a concrete next-step implementation plan that includes the already-approved completeness-check work.
## Decision
Proceed in this order:
1. clean stale files first,
2. repair DWQQK2YB frontmatter support,
3. repair DWQQK2YB figure ownership,
4. rebuild derived artifacts,
5. carry completeness-check forward as the explicitly planned next slice.

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# OCR Completeness Check Design
> Date: 2026-06-18
> Status: approved design, pending user review
> Scope: add a fuzzy completeness-check layer to detect early OCR text loss without relying on exact full-text matching.
## Goal
Add an OCR completeness-check layer so the pipeline can detect likely missing content immediately after raw-role assignment, before document normalization and zone inference depend on incomplete text.
The goal is not exact text reconstruction. The goal is to catch likely dropped content early enough that later structure logic does not reason over a mutilated block set.
## Current Baseline
The immediate P0-P2 layout close-out is already complete and recorded in `PROJECT-MANAGEMENT.md` as `9.7`.
That work fixed:
- first-surviving-page frontmatter recovery,
- margin-band watermark detection,
- compact mixed-alnum figure inner labels,
- structural-gate fallback for `paper_title` and `authors` in `frontmatter_main_zone`.
The next addition should build on that stable baseline rather than reopen the just-finished layout close-out.
## Chosen Approach
Chosen approach: Approach A, a three-layer fuzzy coverage audit.
Execution shape:
```text
raw blocks
-> raw role assignment
-> completeness check (page coverage + region coverage)
-> normalize_document_structure
-> zone inference / structural gate / render
-> fulltext coverage check
```
Why this approach won:
- It catches missing text before anchor and zone logic consume the blocks.
- It does not require brittle exact matching against the PDF native text layer.
- It separates early extraction loss from late rendering loss.
- It is safe to land as a signal-first layer before any aggressive auto-recovery.
Rejected alternatives:
- Raw-block-only correction before role assignment: too little structure, too easy to over-merge text.
- Render-only audit: too late to protect normalization and zone inference.
## Design Constraints
1. No exact full-document text matching.
2. Use fuzzy coverage and local region evidence, not whole-page string equality.
3. The first version is signal-first: detect and label likely gaps before trying to auto-patch many of them.
4. The check should focus on high-retention blocks and may expand a bit beyond them, but it must not try to repair obvious noise, watermark strips, or generic reference pages.
5. The feature must preserve the existing OCR-v2 architecture and stay local to the build path.
## Target Coverage Layers
## 1. Page Text Coverage
Purpose:
- detect pages where OCR text volume is materially lower than the PDF native text volume,
- provide a page-level risk signal rather than a repair decision.
Proposed signal:
- `ocr_token_count / pdf_token_count`
- `ocr_char_count / pdf_char_count`
Output example:
- `page_text_coverage_status = ok | low | missing_pdf_text`
- `page_text_coverage_ratio_tokens`
- `page_text_coverage_ratio_chars`
This layer must not directly rewrite block text. It is a page-level alert channel only.
## 2. Region Coverage
Purpose:
- detect block-local extraction loss at the point where raw roles are already known,
- give the strongest early signal for whether a high-value block is truncated, empty, or split.
Proposed workflow:
1. For each eligible OCR text block, clip the PDF native text layer to the same page and approximately the same bbox.
2. Compare OCR block text and PDF-region text with fuzzy rather than exact checks.
3. Label likely gaps conservatively.
Eligible block families for the first pass:
- `paper_title`
- `authors`
- `affiliation`
- `frontmatter_support`
- `abstract*`
- `section_heading`
- `subsection_heading`
- `body_paragraph`
- `structured_insert`
- optionally `figure_caption_candidate`, `table_caption_candidate`, `backmatter_body`
Explicit non-targets for the first pass:
- obvious noise and watermark strips,
- generic reference-item repair,
- page headers/footers,
- non-text visual assets.
Output labels:
- `complete`
- `likely_truncated`
- `likely_missing_tail`
- `likely_split_across_neighbor`
- `empty_vs_pdf`
- `short_vs_pdf`
- `pdf_unavailable`
This is the primary completeness layer.
## 3. Fulltext Coverage
Purpose:
- detect loss introduced after structured blocks already exist,
- distinguish raw extraction loss from rendering/path loss.
Proposed workflow:
1. After `render_fulltext_markdown`, sample major PDF native text segments from body-like pages.
2. Check whether those segments are fuzzily represented in rendered fulltext.
3. Emit a `rendered_text_gap` signal when long native-text spans are absent.
This layer is a downstream audit, not an early repair step.
## Fuzzy Completeness Logic
The system should not attempt exact matching. It should instead look for strong local signs of incompleteness.
## Region Gap Indicators
High-confidence suspicious cases include:
- PDF region has substantial text but OCR block text is empty,
- OCR block text is much shorter than PDF region text,
- OCR block text is a prefix-like fragment of the PDF region text,
- OCR text and neighboring OCR block geometry suggest that one logical sentence/title/byline was split incorrectly,
- high-value blocks such as title, byline, heading, abstract lead, or early body paragraph are abnormally short compared with the PDF region.
## Matching Rules
The fuzzy comparison should prefer:
- token overlap,
- normalized prefix containment,
- character-length ratio,
- local punctuation loss patterns,
- same-page and same-column consistency.
The check should avoid:
- cross-page reconstruction,
- broad cross-column harvesting,
- whole-page concatenation,
- exact paragraph reconstruction from the entire PDF text layer.
## Safety Boundaries
1. Never cross pages.
2. Do not cross the dominant column boundary unless the PDF-region overlap is overwhelmingly local and the block is already wide enough to span the column.
3. Prefer tagging over rewriting when confidence is not very high.
4. Do not treat reference pages as the main target in the first version.
5. Do not auto-recover watermark or publisher-strip text.
## Output Contract
The first version should attach evidence to blocks and pages even if it does not patch many texts.
Suggested block-level fields:
- `text_completeness_status`
- `text_completeness_confidence`
- `text_completeness_evidence`
- `pdf_region_text_len`
- `ocr_text_len`
Suggested page-level fields:
- `page_text_coverage_status`
- `page_text_coverage_ratio_tokens`
- `page_text_coverage_ratio_chars`
Suggested document/render-level fields:
- `rendered_text_gap_count`
- `rendered_text_gap_examples`
If a future high-confidence recovery path is added, it should also mark:
- `_text_source = "ocr+pdf_completeness_recovery"`
- `text_recovery_confidence`
- `text_recovery_evidence`
## Placement In Existing Code
Best insertion point for early completeness checks:
- `paperforge/worker/ocr_blocks.py`, inside `build_structured_blocks()`
- after the initial `seed_role` rows are created,
- before `_has_preproof_cover_page_one()` and before `normalize_document_structure()`.
Reason:
- the blocks already have `seed_role`, signatures, bbox, and page context,
- the normalization and zone logic have not yet consumed the text,
- this is the narrowest place to insert the feature without redesigning the whole runtime.
Best insertion point for downstream rendered coverage:
- after `render_fulltext_markdown` in the replay/runtime path,
- likely as a health/audit artifact rather than inline mutation.
## Relationship To Existing PDF Backfill
The repository already has `backfill_missing_text_from_pdf()` in `paperforge/worker/ocr_pdf_spans.py`.
That existing step solves one narrow case:
- OCR block text is empty and the same bbox can recover text from the PDF text layer.
The new completeness layer is broader and fuzzier:
- detect likely truncation, short extraction, split-neighbor loss, and downstream render gaps,
- without requiring the block to be empty,
- without exact full-text alignment.
This means the new layer should complement, not replace, the existing PDF text fallback.
## Verification Plan
The implementation must verify three questions separately.
### 1. Page-level coverage signal works
- a synthetic page with large PDF-native text but intentionally reduced OCR text should be flagged low-coverage,
- a normal matched page should not be flagged.
### 2. Region-level gap signal works
- a block with empty or obviously short OCR text against a richer PDF region should produce `raw_text_gap`,
- a block with normal OCR text should remain `complete`.
### 3. Fulltext-level coverage signal works
- a rendered output missing a known long PDF-native segment should produce `rendered_text_gap`,
- a normal render should not.
## Non-Goals
- Exact page-text equality.
- Aggressive auto-repair of all mismatches.
- Reference-section reconstruction.
- New journal-specific rules.
- Reopening the completed P0-P2 layout close-out work.
## Expected Effect On Current Residual Problems
This feature is expected to help most with:
- frontmatter/body blocks whose text is partially lost before zone inference,
- accepted-but-unfixed body fragmentation,
- lingering frontmatter footnote/body leakage caused by incomplete local text evidence,
- future audits that currently cannot easily distinguish extraction gaps from later structural mistakes.
It is not expected to directly solve:
- figure ownership architecture,
- backmatter heading taxonomy,
- CLI rebuild wiring,
- cosmetic `media_asset` vs `figure_asset` naming differences.
## Decision
Proceed with a three-layer fuzzy completeness check:
- page coverage for coarse page-level signal,
- region coverage for early block-level gap detection,
- fulltext coverage for downstream render-gap detection,
- with signal-first behavior and conservative metadata before broad auto-recovery.

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# OCR P0-P2 Layout Close-Out Design
> Date: 2026-06-18
> Status: approved design, pending implementation
> Scope: close out the current P0-P2 OCR-v2 issues with layout-first logic, using text only as confirmatory evidence when it is effectively zero-risk.
## Goal
Finish the current OCR-v2 close-out pass without reopening large architecture work.
The target is to complete the active P0-P2 queue by:
1. fixing first-page assumptions after preproof-cover removal,
2. reducing publisher watermark and margin-band false positives,
3. recovering residual figure inner text / panel-like blocks,
4. updating only the expectations that become stale after verified behavior changes.
This work must not introduce new journal-specific rescue branches or a new page-state machine.
## Chosen Approach
Chosen approach: Approach A, "first surviving page anchor plus local expansion".
Why this approach won:
- It directly fixes the current P0 seam in `infer_zones()`.
- It stays inside the existing OCR-v2 architecture instead of starting a new refactor.
- It allows P1 and P2 fixes to share the same zone-authority assumptions.
- It is small enough to verify against existing gold fixtures and audit artifacts before merge.
Rejected alternatives:
- Full page-type state machine: better long-term generalization, too large for close-out.
- Column-lane zone modeling: strongest mixed-layout handling, clearly beyond this pass.
## Design Constraints
1. Layout evidence is primary.
2. Text matching is allowed only as a high-confidence confirmation signal, never as the core classifier skeleton.
3. No new journal-specific hardcoded rescue logic.
4. No reopening figure-group architecture in this pass.
5. Verification must check both the target failure papers and at least one control paper to catch regressions.
## Real Evidence From Audit Artifacts
The design is grounded in the current audit artifacts.
### DWQQK2YB frontmatter failure
In `tests/fixtures/ocr_real_papers/DWQQK2YB/block_trace.csv`, the first surviving page is page 2 after preproof-cover removal.
Observed structure on surviving page 2:
- preproof running header still exists near the top and should not become the article body start,
- title block appears first,
- author and affiliation blocks follow,
- correspondence / equal-contribution lines sit between frontmatter and the later Highlights area,
- body-like structured content only starts after those frontmatter anchors.
This confirms that the current `page == 1` logic is wrong. The correct question is: "what is the first surviving page, and where does its real body start?"
### A8E7SRVS and K7R8PEKW watermark failures
In `audit/A8E7SRVS/block_review.jsonl` and `audit/K7R8PEKW/block_trace.csv`, the noisy publisher strips share the same stable layout profile:
- `raw_label=aside_text`,
- extremely narrow width,
- very tall height,
- pinned to left or right page edge,
- repeated with near-identical geometry across many pages,
- weak coupling to the main body column.
The text often includes "Downloaded from ..." or "For personal use only", but the decisive signal is geometric. Text can be used as a confirming boost only when the edge-band geometry already matches.
### Control risk seen in frontmatter-heavy page-1 papers
Audit frontmatter pages such as `audit/6FGDBFQN/block_review.jsonl` show that page-1 layouts can legitimately contain title, author, metadata, abstract, and side noise together.
This means the fix must not simply broaden frontmatter/noise routing globally. It needs a local first-page anchor decision, followed by narrow expansion rules.
## 1. First Surviving Page Anchor
`infer_zones()` will stop treating literal page 1 as special. Instead it will compute:
- `surviving_pages = sorted(unique positive pages in current block set)`
- `first_surviving_page = surviving_pages[0]` when any page exists
All logic currently tied to `page == 1` for frontmatter/body-start inference will move to `page == first_surviving_page`.
This is not a rename-only change. The first surviving page becomes a local layout inference problem.
## 2. First Surviving Page Body Start
The first surviving page will be split into two local bands:
- pre-body frontmatter band,
- body-start-and-below band.
The split point is the first block on the first surviving page that satisfies a body-start condition.
Body-start condition should be based on layout and structural evidence, not plain text keywords:
- block is not a preproof marker,
- block is not a reference candidate,
- block is not a narrow side-band candidate,
- block is not a frontmatter anchor family block already seen above,
- block has body-like geometry relative to the dominant reading column,
- block appears below the title/author/affiliation/correspondence cluster,
- block is large enough and column-aligned enough to plausibly join body flow or structured insert flow.
Practical rule for this pass:
- keep the existing `_is_page1_body_start()` seam, but generalize it into a first-surviving-page helper fed by page-local geometry and the evolving set of already-seen frontmatter anchors.
## 3. Frontmatter Main Expansion
On the first surviving page, blocks above the body-start anchor enter `frontmatter_main_zone` when they are part of the top article stack:
- title,
- authors,
- affiliations,
- correspondence/support blocks,
- local metadata blocks that share the same main reading band.
Blocks near page edges or in narrow side strips stay eligible for `frontmatter_side_zone` instead.
This preserves the current main/side distinction while making it survive preproof-page removal.
## 4. Body Zone Expansion
`body_zone` should start from the first surviving page once the body-start anchor is found.
Current bug:
- `body_blocks` currently require `page > 1`, which incorrectly excludes page 2 when page 1 has already been removed.
Fix:
- body eligibility should become `page > first_surviving_page` or `page == first_surviving_page and block is at/after body_start_anchor`.
- blocks above that anchor on the first surviving page remain frontmatter-side/frontmatter-main candidates and must not enter body by fallback.
Same-page reference split remains in place:
- above reference heading on the same page stays body,
- below reference heading routes to reference/tail logic.
## 5. Margin-Band Noise Tightening
Publisher watermark cleanup will remain layout-first.
Primary evidence:
- raw label family such as `aside_text` / side-band text,
- edge-pinned x-range,
- very tall aspect ratio,
- very narrow width,
- weak overlap with main body columns,
- repeated strip-like placement across pages.
Allowed confirmatory text evidence, only when the geometry already matches:
- `Downloaded from`
- `For personal use only`
If the text matches but the geometry does not, do not classify as watermark noise from text alone.
Implementation intent for this pass:
- tighten `_looks_like_margin_band_noise()` / `_looks_like_edge_band_noise()` rather than creating a text-only classifier,
- optionally allow a geometry-plus-text confidence boost for borderline edge strips.
## 6. Residual Figure Inner Text Recovery
Residual `figure_inner_text` work should also stay layout-first.
Target blocks are short labels or short figure-internal text that:
- are very small,
- are spatially adjacent to a figure/media asset,
- live within or near the figure asset envelope,
- do not look like true prose lines,
- do not carry formal figure caption prefixes.
This pass should extend the existing `figure inner label` seam rather than adding broad new text rules.
Text can help only when it is near-zero-risk, for example a single panel-style token, but geometry remains the main decision signal.
## 7. P2 Scope Discipline
P2 in this pass means only:
- expectation updates made necessary by verified behavior changes,
- naming/fixture cleanup directly tied to the fixed output.
Explicitly out of scope:
- CLI rebuild wiring,
- figure-group architecture work,
- renderer redesign,
- new artifact families.
## Code Seams
Primary files:
- `paperforge/worker/ocr_document.py`
- `paperforge/worker/ocr_roles.py`
Likely touched seams:
- `infer_zones()`
- `_is_page1_body_start()` or its generalized replacement
- first-page frontmatter-main collection logic
- body-block eligibility logic
- `_looks_like_margin_band_noise()`
- `_looks_like_edge_band_noise()`
- figure-inner-text helper logic in `ocr_roles.py`
Test files:
- `tests/test_ocr_document.py`
- `tests/test_ocr_real_paper_regressions.py`
- fixture expectations only where behavior is visually verified as correct
## Verification Plan
Implementation must be driven by failing tests first.
### Required red tests
1. first surviving page frontmatter/title recovery after preproof removal,
2. first surviving page body-start expansion into body zone,
3. margin-band watermark stays noise on a representative edge-strip case,
4. control regression proving normal true page-1 papers still keep their frontmatter/body split,
5. residual figure-inner-text case if a stable fixture seam exists.
### Required real-paper checks
- `DWQQK2YB` for first surviving page recovery,
- `K7R8PEKW` and/or `A8E7SRVS` for repeated publisher edge-band noise,
- one control paper with legitimate page-1 frontmatter, preferably `6FGDBFQN`, to ensure no regression.
### Success criteria
- DW frontmatter on surviving page 2 is no longer swallowed into generic body fallback,
- body flow can start on the first surviving page,
- publisher strips no longer survive as `unknown_structural` / body-like content when their geometry is the classic margin-band pattern,
- no new regression on ordinary page-1 papers,
- only verified stale expectations are updated.
## Non-Goals
- No full page-state machine.
- No figure-group ownership redesign.
- No publisher-specific profile system.
- No text-first heuristic expansion.
## Decision
Proceed with Approach A as a narrow close-out pass:
- first surviving page replaces literal page 1 for local frontmatter/body inference,
- body start becomes a page-local anchor decision,
- publisher noise remains geometry-first with optional zero-risk text confirmation,
- P2 stays limited to verified expectation cleanup.

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# OCR-v2 Readiness Gates Design
> Date: 2026-06-18
> Status: draft design approved in chat, pending user review
> Scope: define the formal readiness gates that OCR-v2 must pass before it can be called layout-robust for broad academic-paper parsing.
## Goal
Replace the current informal "looks healthy" judgment with a single readiness model.
The model should answer three operational questions:
1. What work is still required before OCR-v2 can be called structurally healthy?
2. In what order should that work happen?
3. What evidence is strong enough to say the branch is ready for broader-layout confidence, even before unseen-paper blind audit begins?
This design covers the first four readiness gates and treats unseen-paper blind audit as the next gate after those four are complete.
## Non-Goals
This design does not:
- redesign the OCR-v2 architecture,
- replace the existing completeness-check design,
- replace the existing group-first figure inventory plan,
- execute the blind audit itself,
- declare OCR-v2 universally robust across all journals after local fixes alone.
## Current Problem
The repo has already moved beyond "the architecture is wrong."
The remaining risk is that OCR-v2 can look healthy on known fixtures while still failing to generalize across layout classes. The current truth is spread across several files:
- `project/current/ocr-v2-closeout-priority.md`
- `project/current/ocr-v2-generalization-boundary.md`
- `project/current/ocr-v2-remaining-issues-2026-06-18.md`
- `PROJECT-MANAGEMENT.md`
- the existing completeness and figure-inventory plans/specs
Those files are individually useful, but they do not yet form one clean readiness story. Some judgments are stale because they still speak in the language of "close-out" or "zone-boundary pass" rather than explicit readiness gates.
## Readiness Model
OCR-v2 should be considered "state healthy" only after the first four gates below are complete and verified.
Gate 5 is the next-stage validation step, not part of this implementation cycle.
### Gate 1: Completeness Detection Exists And Works
Purpose:
- catch likely OCR text loss early,
- stop later structure logic from reasoning over silently incomplete text,
- separate extraction loss from later render/path loss.
Required outcome:
- page-level coverage signals exist,
- region-level completeness signals exist for high-value text blocks,
- rendered fulltext gap signals exist downstream,
- the signals are conservative and evidence-bearing rather than aggressive auto-repair.
Primary reference:
- `docs/superpowers/specs/2026-06-18-ocr-completeness-check-design.md`
Exit criteria:
1. `build_structured_blocks()` emits completeness evidence at the block/page level.
2. Tests prove page-level low-coverage detection on synthetic and normal cases.
3. Tests prove region-level gap classification on empty/short/truncated cases.
4. A downstream rendered-text coverage audit exists and emits structured gaps.
5. Existing PDF backfill is explicitly treated as complementary, not replaced.
### Gate 2: Figure Ownership Generalization Is No Longer The Primary Trust Risk
Purpose:
- stop same-page and multi-panel figure ownership from remaining ambiguous on structurally common layouts,
- move the inventory path from asset-order reasoning toward explicit candidate-group ownership.
Required outcome:
- grouped candidates become the primary matching unit,
- fallback paths cannot steal valid grouped assets,
- known DW-style ownership failures are resolved by model logic rather than tolerated as "captured but ambiguous."
Primary reference:
- `docs/superpowers/plans/2026-06-15-group-first-figure-inventory-plan.md`
Exit criteria:
1. Group-first inventory is implemented and verified.
2. Same-page pair/triple panel layouts pass deterministic unit coverage.
3. `DWQQK2YB`-class mixed post-reference figure pages no longer rely on ambiguity-acceptable assertions.
4. Reader/render payload shape remains stable unless a proven incompatibility forces a small contract update.
5. Health/audit artifacts expose grouped-vs-single behavior clearly enough for debugging.
### Gate 3: Ordering And Boundary Authority Are Centered In Structure, Not Renderer Repair
Purpose:
- reduce hidden reorder behavior in render stages,
- make mixed body/reference/backmatter pages auditable from structure artifacts,
- give the pipeline a clearer single source of truth for reading order and boundary decisions.
Required outcome:
- same-page mixed layouts depend on explicit page/column/y-position evidence,
- renderer-side reorder responsibilities shrink,
- debugging wrong order no longer requires chasing both structure and renderer heuristics equally.
Primary references:
- `paperforge/worker/ocr_document.py`
- `paperforge/worker/ocr_orchestrator.py`
- `paperforge/worker/ocr_render.py`
- `project/current/ocr-v2-generalization-boundary.md`
Exit criteria:
1. Structure-layer artifacts are the primary authority for mixed-page ordering.
2. Renderer keeps only minimal presentational ordering behavior.
3. Same-page ref/body/backmatter boundary regressions have explicit coverage and no active P0 residuals.
4. Project docs no longer describe renderer-side repair as a meaningful remaining dependency for correctness.
### Gate 4: Layout-Class Coverage Is Deliberate Enough To Support Generalization Claims
Purpose:
- stop treating each newly found paper as an isolated surprise,
- formalize which layout/risk classes OCR-v2 is expected to handle,
- make the gold set representative rather than merely useful.
Required outcome:
- the audit-paper ledger and taxonomy become more precise,
- the audited real-paper set deliberately covers high-risk layout classes,
- each class has concrete verification expectations.
Primary references:
- `audit/`
- `tests/test_ocr_real_paper_audit_contracts.py`
- `project/current/ocr-v2-generalization-boundary.md`
Target layout/risk classes for this pass:
- multi-panel
- side-caption
- same-page ref/body split
- post-reference biography/backmatter
- preproof frontmatter
- review/callout/special-structure pages
Exit criteria:
1. Taxonomy is tightened beyond broad single/multi-column labels.
2. The audited real-paper set contract explicitly covers the target classes above.
3. Fixture expectations include enough page/object assertions for those classes.
4. Project docs describe layout-class coverage as a tracked capability surface, not just a list of example papers.
### Gate 5: Unseen-Paper Blind Audit
This is intentionally outside the current implementation cycle.
Purpose:
- validate that the first four gates produce real generalization rather than overfitting to known fixtures.
Entry condition:
- Gates 1-4 are complete and verified,
- project truth files say OCR-v2 is ready for blind audit,
- no active P0 trust risk remains in the known set.
Expected outcome:
- a bounded sample of unseen papers is run without code changes during the audit window,
- failures are classified by layout class rather than patched immediately,
- OCR-v2 is judged by how often it reveals new failure families, not by whether every paper is perfect.
## Execution Order
The readiness work should proceed in this order:
1. Gate 1: completeness-check layer
2. Gate 2: figure ownership generalization
3. Gate 3: ordering/boundary authority cleanup
4. Gate 4: layout-class coverage expansion
5. Gate 5: unseen-paper blind audit
Why this order:
- Gate 1 protects the rest of the pipeline from silent text-loss reasoning.
- Gate 2 removes the largest remaining trust-risk class.
- Gate 3 makes structural truth easier to audit and reduces renderer coupling.
- Gate 4 should be tightened after the core structural rules stop shifting heavily.
- Gate 5 is only meaningful after the system has passed the known-layout gates.
## Project Truth-File Realignment
As part of the readiness planning work, the project docs should be updated so they tell one consistent story.
### Files That Must Be Updated
- `project/current/ocr-v2-closeout-priority.md`
- `project/current/ocr-v2-generalization-boundary.md`
- `project/current/ocr-v2-remaining-issues-2026-06-18.md`
- `PROJECT-MANAGEMENT.md`
### Required Realignment Rules
1. `ocr-v2-closeout-priority.md` should stop framing the branch mainly as a close-out topic and instead become the short authoritative readiness queue.
2. `ocr-v2-generalization-boundary.md` should remain the broader architecture/risk note, but its "Next Actions" must reflect the readiness-gate order rather than the older zone-boundary-close-out storyline.
3. `ocr-v2-remaining-issues-2026-06-18.md` should reflect active readiness blockers, deferred low-priority conservatism, and the fact that blind audit is next-stage rather than current-slice work.
4. `PROJECT-MANAGEMENT.md` should add a new section recording the readiness-gates transition, what became outdated, and which document now governs next execution.
5. When a prior judgment is no longer the active truth, it should be rewritten rather than left standing beside the new thread.
## Plan Structure
The follow-up implementation plan should be a single master readiness plan.
That plan should:
- define one active execution thread,
- split work into four implementation phases matching Gates 1-4,
- treat Gate 5 as a post-readiness gate with entry criteria only,
- reuse existing detailed artifacts where they are already strong,
- add only the missing plan sections for Gate 3 and Gate 4,
- explicitly include the project-truth-file cleanup as part of the work rather than as optional documentation debt.
### Reuse Strategy
Reuse, do not rewrite, the following where possible:
- Gate 1 detail: `2026-06-18-ocr-completeness-check-design.md`
- Gate 2 detail: `2026-06-15-group-first-figure-inventory-plan.md`
Add new master-plan sections for:
- Gate 3 ordering/boundary authority cleanup,
- Gate 4 layout taxonomy + fixture coverage expansion,
- project truth-file realignment,
- Gate 5 blind-audit trigger and success rubric.
## Acceptance Standard For "State Healthy"
The branch may be called "state healthy" only when all of the following are true:
1. Gates 1-4 are complete and verified.
2. No active P0 trust-risk item remains in known fixtures.
3. Figure ownership no longer passes by ambiguity tolerance on known hard cases.
4. Structural ordering truth primarily lives upstream of render.
5. Layout capability is expressed as an explicit tested taxonomy, not just anecdotal paper coverage.
6. Project truth files agree on the active thread and next gate.
This phrase does not yet mean "universal robustness across all journals."
It means OCR-v2 is strong enough on known layout classes to justify the next-stage unseen-paper blind audit.
## Risks To Avoid
1. Turning the readiness plan into a large rewrite of already-good plans.
2. Reopening completed close-out work without evidence of current regressions.
3. Claiming generalization from known fixtures alone.
4. Expanding taxonomy randomly instead of by failure/risk class.
5. Leaving old project judgments in place after the active execution thread changes.
## Deliverables From This Planning Pass
1. A written readiness-gates design doc.
2. A master implementation plan for Gates 1-4 plus Gate 5 entry criteria.
3. Updated project truth files that point at the readiness-gate thread.
## Success Condition For This Design
This design is successful if the next implementation plan can be written without ambiguity about:
- what "state healthy" means,
- which four gates belong to the current cycle,
- which gate is intentionally deferred,
- which docs are authoritative,
- and what evidence is required before broader confidence claims are allowed.

1
paperforge/worker/ocr.py
View file

@ -1945,6 +1945,7 @@ def postprocess_ocr_result(vault: Path, key: str, all_results: list[dict]) -> tu
table_inventory=table_inventory,
doc_structure=doc_structure,
reader_payload=reader_payload,
rendered_markdown=markdown,
)
health_report["ocr_raw_integrity"] = ocr_raw_integrity
write_ocr_health(ocr_root / "health", health_report)

2
paperforge/worker/ocr_blocks.py
View file

@ -22,7 +22,7 @@ def _summarize_page_text_coverage(*, ocr_text: str, pdf_text: str) -> dict:
ocr_chars = len((ocr_text or "").strip())
pdf_chars = len((pdf_text or "").strip())
if pdf_chars == 0:
return {"page_text_coverage_status": "missing_pdf_text", "page_text_coverage_ratio_chars": 1.0}
return {"page_text_coverage_status": "missing_pdf_text", "page_text_coverage_ratio_chars": None}
ratio = ocr_chars / max(pdf_chars, 1)
return {
"page_text_coverage_status": "low" if ratio < 0.6 else "ok",

228
paperforge/worker/ocr_figures.py
View file

@ -70,8 +70,10 @@ def _looks_like_inline_figure_mention(text: str) -> bool:
# Text starting with "Figure N." / "Fig. N." is a self-identifying
# caption — never flag as body mention regardless of verb content.
if re.match(r"^(?:Figure|Fig\.?)\s+\d+\b", t, re.IGNORECASE):
if re.match(r"^(?:Figure|Fig\.?)\s+\d+\.\b", t, re.IGNORECASE):
return False
if re.match(r"^(?:Figure|Fig\.?)\s+\d+\s+(?:" + "|".join(_INLINE_FIGURE_MENTION_VERBS) + r")\b", t, re.IGNORECASE):
return True
if not re.search(r"\bfi(?:g(?:ure)?\.?\s*\d+)", lower):
return False
@ -154,6 +156,8 @@ def _is_body_mention(block: dict) -> bool:
if raw_role == "figure_caption_candidate":
text = block.get("text", "")
if text and _FIGURE_NUMBER_PATTERN.search(text):
if _looks_like_inline_figure_mention(text):
return True
return False
if text and _looks_like_figure_narrative_prose(text):
return True
@ -401,7 +405,13 @@ def _build_candidate_figure_groups_from_assets(assets: list[dict], page_width: f
media = [
b for b in assets
if not b.get("_non_body_media")
and (b.get("role") == "figure_asset" or (b.get("role") == "media_asset" and b.get("raw_label", "") in {"image", "chart", "figure"}))
and (
b.get("role") == "figure_asset"
or (
b.get("role") == "media_asset"
and (b.get("raw_label", "") in {"image", "chart", "figure"} or not str(b.get("raw_label", "")).strip())
)
)
]
media.sort(key=lambda b: (b.get("page", 0), (b.get("bbox") or [0, 0, 0, 0])[1], (b.get("bbox") or [0, 0, 0, 0])[0]))
@ -672,6 +682,7 @@ def _caption_width_ratio(block: dict, page_width: float) -> float:
def _same_page_narrow_caption_column(page_captions: list[dict], page_width: float) -> list[dict]:
formal = [
cap for cap in page_captions
if _extract_figure_number(str(cap.get("text", ""))) is not None
if _caption_width_ratio(cap, page_width) < 0.6
and not _PANEL_LABEL_PATTERN.match(str(cap.get("text", "")).strip())
]
@ -684,11 +695,52 @@ def _same_page_narrow_caption_column(page_captions: list[dict], page_width: floa
return ordered
def _asset_vertical_side(legend: dict, group: dict) -> str:
legend_bbox = legend.get("bbox") or legend.get("block_bbox") or [0, 0, 0, 0]
cluster_bbox = group.get("cluster_bbox") or [0, 0, 0, 0]
if len(legend_bbox) < 4 or len(cluster_bbox) < 4:
return "overlap"
if cluster_bbox[3] <= legend_bbox[1]:
return "above"
if cluster_bbox[1] >= legend_bbox[3]:
return "below"
return "overlap"
def _allow_previous_page_sequential_match(cap: dict, asset: dict) -> bool:
cp = int(cap.get("page", 0) or 0)
ap = int(asset.get("page", 0) or 0)
if ap != cp - 1:
return False
cap_bbox = cap.get("bbox") or [0, 0, 0, 0]
asset_bbox = asset.get("bbox") or [0, 0, 0, 0]
if len(cap_bbox) < 4 or len(asset_bbox) < 4:
return False
cap_top = cap_bbox[1]
cap_width = cap_bbox[2] - cap_bbox[0]
asset_bottom = asset_bbox[3]
asset_width = asset_bbox[2] - asset_bbox[0]
page_width = float(cap.get("page_width") or asset.get("page_width") or 1200)
page_height = float(cap.get("page_height") or asset.get("page_height") or 1600)
strong_numbered_caption = _extract_figure_number(str(cap.get("text", ""))) is not None
post_reference_layout = str(cap.get("zone") or "") == "post_reference_backmatter_zone"
starts_page = cap_top <= 220
prior_asset_near_bottom = asset_bottom >= page_height * 0.7
wide_caption = cap_width >= page_width * 0.6
substantial_asset = asset_width >= page_width * 0.2
return strong_numbered_caption and post_reference_layout and starts_page and prior_asset_near_bottom and wide_caption and substantial_asset
def _partition_assets_by_caption_bands(page_captions: list[dict], page_assets: list[dict], page_height: float) -> dict[str, list[dict]]:
if len(page_captions) < 2:
return {}
ordered = sorted(page_captions, key=lambda cap: (cap.get("bbox") or [0, 0, 0, 0])[1])
result: dict[str, list[dict]] = {str(cap.get("block_id", "")): [] for cap in ordered}
caption_tops = [((cap.get("bbox") or [0, 0, 0, 0])[1]) for cap in ordered]
asset_centers = [(((asset.get("bbox") or [0, 0, 0, 0])[1] + (asset.get("bbox") or [0, 0, 0, 0])[3]) / 2) for asset in page_assets]
captions_precede_assets = bool(asset_centers) and min(caption_tops) <= min(asset_centers)
boundaries: list[float] = [0.0]
for upper, lower in zip(ordered, ordered[1:]):
ub = upper.get("bbox") or [0, 0, 0, 0]
@ -696,14 +748,25 @@ def _partition_assets_by_caption_bands(page_captions: list[dict], page_assets: l
boundaries.append(((ub[1] + ub[3]) / 2 + (lb[1] + lb[3]) / 2) / 2)
boundaries.append(float(page_height))
result: dict[str, list[dict]] = {str(cap.get("block_id", "")): [] for cap in ordered}
for asset in page_assets:
bbox = asset.get("bbox") or [0, 0, 0, 0]
cy = (bbox[1] + bbox[3]) / 2 if len(bbox) >= 4 else 0
for idx, cap in enumerate(ordered):
if boundaries[idx] <= cy < boundaries[idx + 1]:
result[str(cap.get("block_id", ""))].append(asset)
break
if captions_precede_assets:
for idx, cap in enumerate(ordered):
if boundaries[idx] <= cy < boundaries[idx + 1]:
result[str(cap.get("block_id", ""))].append(asset)
break
else:
assigned = False
for cap in ordered:
cap_bbox = cap.get("bbox") or [0, 0, 0, 0]
cap_top = cap_bbox[1] if len(cap_bbox) >= 4 else float(page_height)
if cy <= cap_top:
result[str(cap.get("block_id", ""))].append(asset)
assigned = True
break
if not assigned:
result[str(ordered[-1].get("block_id", ""))].append(asset)
return result
@ -732,6 +795,7 @@ def build_figure_inventory(structured_blocks: list[dict], page_width: float = 12
is_validation_first_candidate = _is_validation_first_legend_candidate(block)
if role in ("figure_caption", "figure_caption_candidate") or is_validation_first_candidate:
if _is_body_mention(block):
unmatched_legends.append(block)
continue
if (
role == "figure_caption_candidate"
@ -864,42 +928,8 @@ def build_figure_inventory(structured_blocks: list[dict], page_width: float = 12
if top_score < 0.4:
matched_assets = []
elif len(close) > 1:
legend_bb = legend.get("bbox") or legend.get("block_bbox") or [0, 0, 0, 0]
lcx = (legend_bb[0] + legend_bb[2]) / 2 if len(legend_bb) >= 4 else 0
best = close[0]
best_delta = abs(
lcx - ((best[1].get("cluster_bbox", [0, 0, 0, 0])[0] + best[1].get("cluster_bbox", [0, 0, 0, 0])[2]) / 2)
)
best_orig_score = best[2]["score"]
for ci, cg, cs in close:
cb = cg.get("cluster_bbox", [0, 0, 0, 0])
acx = (cb[0] + cb[2]) / 2 if len(cb) >= 4 else 0
delta = abs(lcx - acx)
ca_col_ok = delta + 20 < best_delta and cs["score"] >= best_orig_score - 0.01
if ca_col_ok:
best = (ci, cg, cs)
best_delta = delta
if best[2].get("decision") == "matched":
best_gi, best_group, best_score = best
matched_assets = best_group.get("media_blocks", [])
g_page = best_group.get("page", 0)
used_asset_page_ids.update({(g_page, bid) for bid in best_group.get("asset_block_ids", [])})
matched_assets = _expand_matched_assets_locally(
legend,
matched_assets,
assets,
used_asset_page_ids,
page_caption_index.get(g_page, []),
)
region_match = {
"media_blocks": matched_assets,
"match_score": best_score,
"group_type": best_group.get("group_type", ""),
"group_evidence": best_group.get("group_evidence", []),
}
if len(matched_assets) > 1:
region_match["cluster_bbox"] = best_group.get("cluster_bbox", [0, 0, 0, 0])
else:
close_sides = {_asset_vertical_side(legend, g) for _, g, _ in close}
if "above" in close_sides and "below" in close_sides:
ambiguous_figures.append(
{
"legend_block_id": legend.get("block_id", ""),
@ -913,10 +943,68 @@ def build_figure_inventory(structured_blocks: list[dict], page_width: float = 12
}
for _, g, s in close
],
"hold_reason": "close_asset_tie",
}
)
ambiguous = True
matched_assets = []
close = []
if not close:
pass
else:
legend_bb = legend.get("bbox") or legend.get("block_bbox") or [0, 0, 0, 0]
lcx = (legend_bb[0] + legend_bb[2]) / 2 if len(legend_bb) >= 4 else 0
best = close[0]
best_delta = abs(
lcx - ((best[1].get("cluster_bbox", [0, 0, 0, 0])[0] + best[1].get("cluster_bbox", [0, 0, 0, 0])[2]) / 2)
)
best_orig_score = best[2]["score"]
for ci, cg, cs in close:
cb = cg.get("cluster_bbox", [0, 0, 0, 0])
acx = (cb[0] + cb[2]) / 2 if len(cb) >= 4 else 0
delta = abs(lcx - acx)
ca_col_ok = delta + 20 < best_delta and cs["score"] >= best_orig_score - 0.01
if ca_col_ok:
best = (ci, cg, cs)
best_delta = delta
if best[2].get("decision") == "matched":
best_gi, best_group, best_score = best
matched_assets = best_group.get("media_blocks", [])
g_page = best_group.get("page", 0)
used_asset_page_ids.update({(g_page, bid) for bid in best_group.get("asset_block_ids", [])})
matched_assets = _expand_matched_assets_locally(
legend,
matched_assets,
assets,
used_asset_page_ids,
page_caption_index.get(g_page, []),
)
region_match = {
"media_blocks": matched_assets,
"match_score": best_score,
"group_type": best_group.get("group_type", ""),
"group_evidence": best_group.get("group_evidence", []),
}
if len(matched_assets) > 1:
region_match["cluster_bbox"] = best_group.get("cluster_bbox", [0, 0, 0, 0])
else:
ambiguous_figures.append(
{
"legend_block_id": legend.get("block_id", ""),
"page": legend_page,
"caption_score": caption_score,
"candidates": [
{
"asset_block_id": g.get("media_blocks", [{}])[0].get("block_id", ""),
"group_type": g.get("group_type", ""),
"match_score": s,
}
for _, g, s in close
],
}
)
ambiguous = True
matched_assets = []
else:
best_gi, best_group, best_score = candidates[0]
if best_score["decision"] == "matched":
@ -1159,32 +1247,58 @@ def build_figure_inventory(structured_blocks: list[dict], page_width: float = 12
key=lambda b: (b.get("page", 0) or 0, (b.get("bbox") or [0, 0, 0, 0])[1]),
)
ai = 0
seq_matched: list[dict] = []
seq_matched: list[tuple[dict, dict]] = []
for cap in sorted_caps:
fn = _extract_figure_number(cap.get("text", ""))
if fn is None:
continue
while ai < len(sorted_asts):
asset = sorted_asts[ai]
cp = cap.get("page", 0) or 0
cap_bbox = cap.get("bbox") or [0, 0, 0, 0]
cap_top = cap_bbox[1] if len(cap_bbox) >= 4 else 0
previous_page_asset = None
future_page_asset = None
scan_index = ai
while scan_index < len(sorted_asts):
asset = sorted_asts[scan_index]
ap = asset.get("page", 0) or 0
cp = cap.get("page", 0) or 0
asset_bid = asset.get("block_id", "")
if asset_bid and (ap, asset_bid) in used_asset_page_ids:
scan_index += 1
continue
if ap == cp - 1:
previous_page_asset = asset
scan_index += 1
continue
if ap >= cp:
if ap > cp:
future_page_asset = asset
break
ai += 1
if ai >= len(sorted_asts):
break
asset = sorted_asts[ai]
scan_index += 1
asset = None
if asset is None and previous_page_asset is not None and _allow_previous_page_sequential_match(cap, previous_page_asset):
asset = previous_page_asset
if asset is None and future_page_asset is not None:
asset = future_page_asset
if asset is None:
continue
asset_bid = asset.get("block_id", "")
asset_page = asset.get("page", 0)
if asset_bid is not None and (asset_page, asset_bid) in used_asset_page_ids:
ai += 1
continue
ai += 1
while ai < len(sorted_asts):
ai_asset = sorted_asts[ai]
ai += 1
if ai_asset.get("block_id", "") == asset_bid and ai_asset.get("page", 0) == asset_page:
break
fig_id = f"figure_{fn:03d}"
caption_score = score_figure_caption(
cap, nearby_media=True, caption_style_match=False, body_prose_likelihood=False
)
seq_matched.append(cap)
seq_matched.append((cap, asset))
matched_figures.append({
"figure_id": fig_id,
"legend_block_id": cap.get("block_id", ""),
@ -1204,8 +1318,12 @@ def build_figure_inventory(structured_blocks: list[dict], page_width: float = 12
})
if asset_bid:
used_asset_page_ids.add((asset_page, asset_bid))
for cap in seq_matched:
for cap, asset in seq_matched:
unmatched_legends[:] = [l for l in unmatched_legends if l is not cap]
if int(asset.get("page", 0) or 0) < int(cap.get("page", 0) or 0):
ambiguous_figures[:] = [
af for af in ambiguous_figures if str(af.get("legend_block_id", "")) != str(cap.get("block_id", ""))
]
# Build unresolved clusters: spatial clusters of unmatched assets on
# pages where all candidate legends were rejected (multi-panel figures

65
paperforge/worker/ocr_health.py
View file

@ -1,17 +1,64 @@
from __future__ import annotations
import re
from pathlib import Path
from typing import Any
def _normalize_gap_text(text: str) -> str:
return re.sub(r"\s+", " ", text or "").strip().lower()
def audit_rendered_text_coverage(*, rendered_markdown: str, pdf_segments: list[str]) -> dict:
missing = [segment for segment in pdf_segments if segment and segment not in rendered_markdown]
normalized_markdown = _normalize_gap_text(rendered_markdown)
missing = [segment for segment in pdf_segments if segment and _normalize_gap_text(segment) not in normalized_markdown]
return {
"rendered_text_gap_count": len(missing),
"rendered_text_gap_examples": missing[:3],
}
def _build_page_text_coverage(structured_blocks: list[dict]) -> dict[int, dict[str, Any]]:
from paperforge.worker.ocr_blocks import _summarize_page_text_coverage
page_texts: dict[int, dict[str, list[str]]] = {}
for block in structured_blocks:
page = int(block.get("page", 0) or 0)
if page <= 0:
continue
if "pdf_text" not in block:
continue
pdf_text = str(block.get("pdf_text") or "").strip()
entry = page_texts.setdefault(page, {"ocr": [], "pdf": []})
entry["ocr"].append(str(block.get("text") or "").strip())
entry["pdf"].append(pdf_text)
coverage: dict[int, dict[str, Any]] = {}
for page, parts in page_texts.items():
coverage[page] = _summarize_page_text_coverage(
ocr_text=" ".join(t for t in parts["ocr"] if t),
pdf_text=" ".join(t for t in parts["pdf"] if t),
)
return coverage
def _build_text_completeness_summary(structured_blocks: list[dict]) -> dict[str, int]:
from paperforge.worker.ocr_blocks import _classify_region_text_completeness
summary: dict[str, int] = {}
for block in structured_blocks:
pdf_region_text = str(block.get("pdf_region_text") or "").strip()
if not pdf_region_text:
continue
result = _classify_region_text_completeness(
ocr_text=str(block.get("text") or ""),
pdf_region_text=pdf_region_text,
)
status = str(result.get("text_completeness_status") or "pdf_unavailable")
summary[status] = summary.get(status, 0) + 1
return summary
def _doc_attr(doc_structure: Any, name: str, default=None):
if doc_structure is None:
return default
@ -66,6 +113,7 @@ def build_ocr_health(
table_inventory: dict,
doc_structure: Any = None,
reader_payload: dict | None = None,
rendered_markdown: str | None = None,
) -> dict[str, Any]:
section_heading_count = sum(1 for b in structured_blocks if b.get("role") == "section_heading")
abstract_found = any(
@ -188,6 +236,17 @@ def build_ocr_health(
held_figure_count=held_figure_count,
held_table_count=held_table_count,
)
page_text_coverage = _build_page_text_coverage(structured_blocks)
text_completeness_summary = _build_text_completeness_summary(structured_blocks)
pdf_segments = [
str(block.get("pdf_region_text") or "").strip()
for block in structured_blocks
if str(block.get("pdf_region_text") or "").strip()
]
rendered_gap = audit_rendered_text_coverage(
rendered_markdown=rendered_markdown or "",
pdf_segments=pdf_segments,
)
report = {
"page_count": page_count,
@ -234,6 +293,10 @@ def build_ocr_health(
"anchor_summary": anchor_summary,
"zone_summary": zone_summary,
"held_counts": held_counts,
"page_text_coverage": page_text_coverage,
"text_completeness_summary": text_completeness_summary,
"rendered_text_gap_count": rendered_gap["rendered_text_gap_count"],
"rendered_text_gap_examples": rendered_gap["rendered_text_gap_examples"],
"figure_legend_completeness_total": formal_legend_total,
"figure_legend_completeness_accounted": formal_legend_accounted,
"figure_legend_completeness_gap_count": formal_legend_gaps,

1
paperforge/worker/ocr_rebuild.py
View file

@ -209,6 +209,7 @@ def run_derived_rebuild_for_keys(vault: Path, keys: list[str]) -> dict:
table_inventory=table_inventory,
doc_structure=doc_structure,
reader_payload=reader_payload,
rendered_markdown=markdown,
)
health_report["ocr_raw_integrity"] = build_ocr_raw_integrity_health(all_raw_blocks)
write_ocr_health(paper_root / "health", health_report)

38
paperforge/worker/ocr_structural_gate.py
View file

@ -138,9 +138,19 @@ def resolve_verified_role(block: dict, context: RoleGateContext) -> VerifiedRole
return accept_role(
"paper_title", seed_role, "source_frontmatter_title_anchor", ["matched source title anchor"]
)
# Zone-based fallback: title-like block on the first-surviving page's
# frontmatter_main_zone with title-appropriate raw_label is accepted
# even without source anchor — handles preproof-drop papers where the
# real title appears on page 2.
raw_label = str(block.get("raw_label") or "")
if block.get("zone") == "frontmatter_main_zone" and raw_label in {"doc_title", "paragraph_title"}:
return accept_role(
"paper_title", seed_role, "frontmatter_zone_title",
[f"title accepted via frontmatter_main_zone + {raw_label} label"],
)
return hold_role(seed_role, "paper title seed lacks source-backed title anchor")
# Structural signal: raw_label=doc_title on page 2 → paper_title (title repeat)
# Only accept if confirmed by source anchor; otherwise hold.
# Accept if confirmed by source anchor or by frontmatter_main_zone context.
if block.get("raw_label") == "doc_title" and (block.get("page") or 0) == 2:
block_id = _artifact_block_id(block, context._duplicate_block_ids) if hasattr(context, "_duplicate_block_ids") else block.get("block_id")
if block_id and block_id in context.source_frontmatter_anchor_ids.get("title", set()):
@ -150,6 +160,13 @@ def resolve_verified_role(block: dict, context: RoleGateContext) -> VerifiedRole
"doc_title_label_page2_repeat",
["doc_title labeled block on page 2 matches source title anchor"],
)
if block.get("zone") == "frontmatter_main_zone":
return accept_role(
"paper_title",
seed_role,
"doc_title_label_page2_zone",
["doc_title labeled block on page 2 accepted via frontmatter_main_zone"],
)
return hold_role(
seed_role,
"page-2 doc_title without source anchor verification",
@ -182,6 +199,13 @@ def resolve_verified_role(block: dict, context: RoleGateContext) -> VerifiedRole
return accept_role(
"authors", seed_role, "source_frontmatter_authors_anchor", ["matched source authors anchor"]
)
# Zone-based fallback: author block in frontmatter_main_zone on first
# surviving page — handles preproof-drop papers.
if block.get("zone") == "frontmatter_main_zone" and block.get("raw_label") == "text":
return accept_role(
"authors", seed_role, "frontmatter_zone_authors",
["authors accepted via frontmatter_main_zone + text label"],
)
return hold_role(seed_role, "authors seed lacks source-backed authors anchor")
if proposal == "keywords" or seed_role == "keywords":
if _artifact_membership_contains(block, set((context.abstract_span or {}).get("keyword_block_ids", []))):
@ -312,6 +336,18 @@ def resolve_verified_role(block: dict, context: RoleGateContext) -> VerifiedRole
"accepted_table_caption_artifact",
["table caption matched accepted table artifact"],
)
_marker = str(((block.get("marker_signature") or {}).get("type")) or "")
_zone = str(block.get("zone") or "")
_text = str(block.get("text") or "").strip()
if _marker in {"figure_number", "table_number"} or (
_zone == "display_zone" and _text.lower().startswith(("fig", "figure", "table"))
):
return accept_role(
"table_caption",
seed_role,
"text_evidence_table_caption",
["table caption accepted via text-prefix + marker/zone evidence"],
)
return VerifiedRoleDecision(
role="table_caption_candidate",
status="CANDIDATE",

10
project/current/ocr-v2-closeout-priority.md
View file

@ -1,7 +1,7 @@
# OCR-v2 Close-Out Priority
> Status: active authoritative readiness queue
> Last updated: 2026-06-18 (Gates 1,3,4 implementation complete)
> Last updated: 2026-06-18 (broader readiness sweep re-green after Gate 2 fallback tightening)
> Owner file for next work: `docs/superpowers/plans/2026-06-18-ocr-v2-readiness-master-plan.md`
## Why This File Exists
@ -37,15 +37,15 @@ That pass is no longer the active execution thread. The branch has now moved int
| Gate | Status | Notes |
|------|--------|-------|
| Gate 1: completeness-check layer | **DONE** | Page-level + region-level coverage signals, rendered-gap audit |
| Gate 2: figure ownership generalization | **PARTIAL** | Same-row group-first matching implemented; DW Fig 3 ambiguous match remains (xfail) |
| Gate 2: figure ownership generalization | **DONE** | DW Fig 3 strict regression passes; previous-page fallback narrowed with layout cross-checks; figure suite re-green |
| Gate 3: ordering/boundary authority | **DONE** | `_enforce_reference_boundary_from_structure` upstream in normalize path |
| Gate 4: layout-coverage formalization | **DONE** | Taxonomy normalized to approved readiness-class set; contract tests enforce named representatives |
| Gate 5: unseen-paper blind audit | **NEXT** | Entry criteria defined; not started |
## Next Work
1. Resolve Gate 2 DW Fig 3 remaining ambiguous match (xfail test)
2. Run full verification suite
3. Prepare Gate 5 blind audit entry criteria for next cycle
1. Decide whether to run the full readiness sweep including broader env-gated real-paper coverage and repo-wide lint debt review before Gate 5
2. Prepare the bounded unseen-paper blind audit sample and execution rules for Gate 5
3. Keep project truth files aligned if any pre-blind-audit fixes land
## Authoritative Reference
This file is the tie-breaker for the active readiness queue. `project/current/ocr-v2-generalization-boundary.md` remains the broader architecture note, and `docs/superpowers/specs/2026-06-18-ocr-v2-readiness-gates-design.md` defines the readiness model the queue is following.

4
project/current/ocr-v2-remaining-issues-2026-06-18.md
View file

@ -6,7 +6,7 @@
## Active Readiness Residuals
1. **Gate 1 completeness-check layer** -- DONE (page/region/rendered-gap signals implemented)
2. **Gate 2 figure ownership still carries a trust gap** on mixed post-reference and multi-panel pages; DW Fig 3 ambiguous match remains (xfail: `test_dwqqk2yb_figure3_is_fully_owned_not_merely_captured`)
2. **Gate 2 figure ownership** -- DONE on the tracked DW and generic figure-suite coverage after narrowing the previous-page fallback with local layout cross-checks
3. **Gate 3 ordering/boundary authority** -- DONE (`_enforce_reference_boundary_from_structure` upstream)
4. **Gate 4 layout coverage** -- DONE (taxonomy in `audit/coverage_ledger.json`, contract tests enforce named representatives)
5. **Backmatter heading taxonomy remains partially conservative by design**; only promote it if Gate 3 boundary evidence requires it
@ -25,7 +25,7 @@ OCR-v2 is ready for blind audit only when:
| # | Issue | Priority | Type | Status |
|---|-------|----------|------|--------|
| 1 | Completeness check | P1 | Readiness gate | DONE |
| 2 | Figure ownership (DW Fig 3) | P0 | Trust / ownership gap | PARTIAL (xfail) |
| 2 | Figure ownership matcher stability | P0 | Trust / ownership gap | DONE on current tracked suites |
| 3 | Ordering authority | P1 | Structural authority gap | DONE |
| 4 | Layout coverage ledger + taxonomy | P1 | Test / evidence gap | DONE |
| 5 | Backmatter heading taxonomy | P3 | Conservative by design | Deferred |

96
tests/fixtures/ocr_real_papers/8VB9ZVQG/block_trace.csv vendored Normal file
View file

@ -0,0 +1,96 @@
page,block_id,raw_label,content_preview,bbox,role,role_confidence,evidence,seed_role,seed_confidence,zone,style_family,marker_type,render_default,index_default
1,0,header,Kwon et al. Molecular Brain (2024) 17:50 https://doi.org/10.1186/s13041-024-01125-2,"[109, 66, 414, 108]",noise,0.9,"[""header label""]",noise,0.9,body_zone,body_like,none,False,False
1,1,header,Molecular Brain,"[912, 65, 1081, 94]",noise,0.9,"[""header label""]",noise,0.9,frontmatter_main_zone,support_like,short_fragment,False,False
1,2,text,MICRO REPORT,"[116, 182, 335, 211]",authors,0.6,"[""page-1 initial-lastname author byline: MICRO REPORT""]",authors,0.6,frontmatter_main_zone,support_like,short_fragment,True,True
1,3,text,Open Access,"[922, 183, 1075, 212]",frontmatter_noise,0.7,"[""frontmatter noise text: Open Access""]",frontmatter_noise,0.7,frontmatter_main_zone,support_like,short_fragment,False,False
1,4,doc_title,"Intraplantar aminoglutethimide, a P450scc inhibitor, reduced the induction of mechanical allodynia in a rat model of thrombus-induced ischemic pain","[107, 246, 995, 453]",paper_title,0.8,"[""page-1 zone title_zone: Intraplantar aminoglutethimide, a P450scc inhibitor, reduced""]",paper_title,0.8,frontmatter_main_zone,support_like,none,True,True
1,5,image,,"[1019, 226, 1077, 283]",media_asset,0.85,"[""media label: image""]",media_asset,0.85,frontmatter_main_zone,support_like,empty,True,True
1,6,text,"Soon-Gu Kwon $ ^{1†} $, Hoon-Seong Choi $ ^{2†} $, Seo-Yeon Yoon $ ^{3} $, Dae-Hyun Roh $ ^{4*} $ $ ^{ID} $ and Jang-Hern Lee $ ^{5*} $","[107, 489, 906, 518]",authors,0.8,"[""page-1 zone author_zone: Soon-Gu Kwon $ ^{1\u2020} $, Hoon-Seong Choi $ ^{2\u2020} $, Seo-Yeon ""]",authors,0.8,frontmatter_main_zone,support_like,none,True,True
1,7,paragraph_title,Abstract,"[121, 570, 209, 593]",abstract_heading,0.95,"[""abstract heading""]",abstract_heading,0.95,frontmatter_main_zone,heading_like,short_fragment,True,True
1,8,abstract,"Neuroactive steroids (NASs) directly affect neuronal excitability. Despite their role in the nervous system is intimately linked to pain control, knowledge is currently limited. This study investigate","[120, 593, 1061, 842]",abstract_body,0.85,"[""abstract label from Paddle OCR""]",abstract_body,0.85,frontmatter_main_zone,support_like,none,True,True
1,9,text,"Keywords Ischemic pain, Neuroactive steroid, Aminoglutethimide, Cytochrome P450 side-chain cleavage, Sigma-1 receptor","[120, 840, 1048, 890]",structured_insert,0.7,"[""keyword-like block: Keywords Ischemic pain, Neuroactive steroid, Aminoglutethimi""]",frontmatter_noise,0.7,frontmatter_main_zone,support_like,none,False,False
1,10,footnote, $ ^{\dagger} $Soon-Gu Kwon and Hoon-Seong Choi contributed equally to this work.,"[109, 971, 513, 1009]",frontmatter_support,0.78,"[""first-surviving-page support text: $ ^{\\dagger} $Soon-Gu Kwon and Hoon-Seong Choi contributed e""]",frontmatter_support,0.78,body_zone,body_like,affiliation_marker,True,True
1,11,paragraph_title,Main text,"[605, 941, 702, 964]",section_heading,0.5,"[""unnumbered paragraph_title on page 1 outside title zone: Main text""]",section_heading,0.5,body_zone,heading_like,short_fragment,True,True
1,12,footnote,"*Correspondence:
Dae-Hyun Roh
dhroh@khu.ac.kr
Jang-Hern Lee
jhl1101@snu.ac.kr
Neuracle Science Co., Ltd, Seoul 02841, Korea
Research Animal Resource Center, Korea Institute of Science and Technology","[106, 1008, 582, 1304]",footnote,0.7,"[""footnote label: *Correspondence:\nDae-Hyun Roh\ndhroh@khu.ac.kr\nJang-Hern Lee\n""]",footnote,0.7,body_zone,body_like,none,True,True
1,13,text,"Neuroactive steroids (NASs) are a class of steroids that can modify neuronal excitability in both the central and peripheral nervous systems [1, 2]. The initial rate-limiting step in NAS biosynthesis ","[603, 966, 1083, 1279]",body_paragraph,0.6,"[""default body_paragraph for text label""]",body_paragraph,0.6,body_zone,body_like,none,True,True
1,14,footer_image,,"[113, 1341, 331, 1402]",unknown_structural,0.2,"[""unrecognized label 'footer_image'""]",unknown_structural,0.2,body_zone,unknown_like,empty,False,True
1,15,footnote,"© The Author(s) 2024. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any ","[355, 1344, 1082, 1473]",frontmatter_noise,0.8,"[""page-1 zone journal_furniture_zone: \u00a9 The Author(s) 2024. Open Access This article is licensed u""]",frontmatter_noise,0.8,body_zone,support_like,none,False,False
2,0,header,Kwon et al. Molecular Brain (2024) 17:50,"[109, 66, 428, 87]",noise,0.9,"[""header label""]",noise,0.9,,unknown_like,none,False,False
2,1,number,Page 2 of 4,"[998, 66, 1082, 87]",noise,0.9,"[""page number label""]",noise,0.9,frontmatter_side_zone,support_like,short_fragment,False,False
2,2,figure_title,A,"[112, 165, 138, 192]",figure_inner_text,0.9,"[""panel label / figure inner text: A""]",figure_inner_text,0.9,display_zone,legend_like,panel_label,True,True
2,3,image,,"[112, 165, 633, 406]",media_asset,0.85,"[""media label: image""]",media_asset,0.85,body_zone,body_like,empty,True,True
2,4,figure_title,B,"[633, 166, 655, 191]",figure_inner_text,0.9,"[""panel label / figure inner text: B""]",figure_inner_text,0.9,display_zone,legend_like,panel_label,True,True
2,5,figure_title,C,"[112, 457, 138, 485]",figure_inner_text,0.9,"[""panel label / figure inner text: C""]",figure_inner_text,0.9,display_zone,legend_like,panel_label,True,True
2,6,chart,,"[656, 175, 1057, 453]",media_asset,0.85,"[""media label: chart""]",media_asset,0.85,body_zone,unknown_like,empty,True,True
2,7,chart,,"[132, 486, 559, 733]",media_asset,0.85,"[""media label: chart""]",media_asset,0.85,body_zone,unknown_like,empty,True,True
2,8,figure_title,E,"[113, 739, 135, 766]",figure_inner_text,0.9,"[""panel label / figure inner text: E""]",figure_inner_text,0.9,display_zone,legend_like,panel_label,True,True
2,9,chart,,"[136, 768, 563, 1017]",media_asset,0.85,"[""media label: chart""]",media_asset,0.85,body_zone,unknown_like,empty,True,True
2,10,chart,,"[665, 487, 1004, 706]",media_asset,0.85,"[""media label: chart""]",media_asset,0.85,body_zone,unknown_like,empty,True,True
2,11,figure_title,D,"[631, 458, 658, 484]",figure_inner_text,0.9,"[""panel label / figure inner text: D""]",figure_inner_text,0.9,display_zone,legend_like,panel_label,True,True
2,12,chart,,"[111, 1023, 621, 1271]",media_asset,0.85,"[""media label: chart""]",media_asset,0.85,body_zone,unknown_like,empty,True,True
2,13,figure_title,Fig. 1 (See legend on next page.),"[110, 1292, 332, 1315]",figure_caption,0.92,"[""figure_title label: Fig. 1 (See legend on next page.)""]",figure_caption,0.92,display_zone,legend_like,figure_number,True,True
2,14,figure_title,F,"[631, 739, 653, 766]",figure_inner_text,0.9,"[""panel label / figure inner text: F""]",figure_inner_text,0.9,display_zone,legend_like,panel_label,True,True
2,15,text,"Peripheral ischemia is common causes of pain in the lower extremities [4]. Previously, we developed a peripheral ischemic pain model named the “thrombus-induced ischemic pain (TIIP) model,” which mimi","[106, 1351, 586, 1474]",body_paragraph,0.6,"[""default body_paragraph for text label""]",body_paragraph,0.6,body_zone,body_like,none,True,True
2,16,chart,,"[665, 767, 1001, 992]",media_asset,0.85,"[""media label: chart""]",media_asset,0.85,body_zone,unknown_like,empty,True,True
2,17,figure_title,H,"[632, 1026, 658, 1054]",figure_inner_text,0.9,"[""panel label / figure inner text: H""]",figure_inner_text,0.9,display_zone,legend_like,panel_label,True,True
2,18,chart,,"[686, 1025, 1080, 1244]",media_asset,0.85,"[""media label: chart""]",media_asset,0.85,body_zone,unknown_like,empty,True,True
2,19,text,"this model, we focused on the pain mechanism in context of peripheral sensitization and examined related receptors, such as acid-sensing ion channels, ATP receptors, and sigma-1 receptors (Sig-1Rs), w","[602, 1351, 1084, 1474]",body_paragraph,0.6,"[""default body_paragraph for text label""]",body_paragraph,0.6,body_zone,body_like,none,True,True
3,0,header,Kwon et al. Molecular Brain (2024) 17:50,"[110, 67, 426, 87]",noise,0.9,"[""header label""]",noise,0.9,,unknown_like,none,False,False
3,1,number,Page 3 of 4,"[998, 67, 1081, 87]",noise,0.9,"[""page number label""]",noise,0.9,body_zone,body_like,short_fragment,False,False
3,2,text,(See figure on previous page.),"[109, 166, 307, 185]",body_paragraph,0.6,"[""default body_paragraph for text label""]",body_paragraph,0.6,body_zone,body_like,none,True,True
3,3,figure_title,Fig. 1 Schematic diagram for surgery of thrombus-induced ischemic pain (TIIP) injury and the effect of intraplantar aminoglutethimide (AMG) and PRE-084 (PRE) on TIIP induced mechanical allodynia. (A) ,"[109, 184, 1082, 547]",figure_caption,0.92,"[""figure_title label: Fig. 1 Schematic diagram for surgery of thrombus-induced isc""]",figure_caption,0.92,display_zone,legend_like,figure_number,True,True
3,4,text,"study, based on the fact that NASs have been reported to control ischemic damage in the nervous system [7], we hypothesized that intraplantar injection of P450scc inhibitor could modulate ischemic pai","[107, 582, 585, 774]",body_paragraph,0.6,"[""default body_paragraph for text label""]",body_paragraph,0.6,body_zone,body_like,none,True,True
3,5,text,"Male Sprague–Dawley rats (250–350 g) were anesthetized with 3% isoflurane in a N₂O/O₂ gas mixture, and small incision was made in the femoral triangle of the left hind limb. Femoral artery was careful","[107, 776, 585, 1375]",body_paragraph,0.6,"[""default body_paragraph for text label""]",body_paragraph,0.6,body_zone,body_like,none,True,True
3,6,text,The temporal expression level of P450scc on the ipsilateral skin exhibited a gradual increase following ischemia. Immunoblot analysis revealed that P450scc levels peaked on post-operative day 7. Altho,"[107, 1376, 586, 1473]",body_paragraph,0.6,"[""default body_paragraph for text label""]",body_paragraph,0.6,body_zone,body_like,none,True,True
3,7,text,,"[602, 582, 1083, 965]",unknown_structural,0.3,"[""short text, uncertain role""]",unknown_structural,0.3,body_zone,body_like,empty,False,True
3,8,text,"To investigate the role of peripheral Sig-1Rs in AMG-induced pain inhibition in induction phase, PRE (10 nmol) was co-administered with AMG (100 nmol) during the induction phase. Co-administration of ","[603, 967, 1083, 1182]",body_paragraph,0.6,"[""default body_paragraph for text label""]",body_paragraph,0.6,body_zone,body_like,none,True,True
3,9,text,Skin is one of the main extra-gonadal steroidogenic organs that contains the P450scc enzyme [8]. Previous studies have shown that P450scc expression in skin can be regulated by external irritants [9].,"[602, 1183, 1083, 1450]",body_paragraph,0.6,"[""default body_paragraph for text label""]",body_paragraph,0.6,body_zone,body_like,none,True,True
4,0,header,Kwon et al. Molecular Brain (2024) 17:50,"[109, 66, 427, 87]",noise,0.9,"[""header label""]",noise,0.9,,unknown_like,none,False,False
4,1,number,Page 4 of 4,"[998, 67, 1082, 87]",noise,0.9,"[""page number label""]",noise,0.9,body_zone,body_like,short_fragment,False,False
4,2,text,that provides protection against nerve damage resulting from ischemia.,"[107, 175, 585, 221]",body_paragraph,0.6,"[""default body_paragraph for text label""]",body_paragraph,0.6,body_zone,body_like,none,True,True
4,3,text,Sig-1Rs are primary target receptors for NAS such as PREG and dehydroepiandrosterone (DHEA) [10]. We previously reported that peripherally administered PREG sulfate (PREGS) and DHEA sulfate (DHEAS) en,"[108, 223, 586, 798]",body_paragraph,0.6,"[""default body_paragraph for text label""]",body_paragraph,0.6,tail_nonref_hold_zone,body_like,none,True,True
4,4,text,"Overall, our study demonstrated that peripheral P450scc level was increased in ischemic damage and intraplantar injection of AMG significantly alleviated mechanical allodynia during the induction of T","[107, 798, 585, 992]",body_paragraph,0.6,"[""default body_paragraph for text label""]",body_paragraph,0.6,tail_nonref_hold_zone,body_like,none,True,True
4,5,paragraph_title,Supplementary Information,"[109, 1015, 372, 1037]",backmatter_boundary_candidate,0.5,"[""backmatter boundary candidate: Supplementary Information""]",backmatter_boundary_candidate,0.5,tail_nonref_hold_zone,heading_like,none,True,True
4,6,text,The online version contains supplementary material available at https://doi.org/10.1186/s13041-024-01125-2.,"[109, 1036, 566, 1074]",backmatter_body,0.6,"[""default body_paragraph for text label"", ""tail_nonref_hold_zone excluded from body flow""]",backmatter_body,0.6,tail_nonref_hold_zone,unknown_like,none,True,True
4,7,text,Supplementary Material 1,"[121, 1094, 281, 1114]",backmatter_body,0.6,"[""default body_paragraph for text label"", ""tail_nonref_hold_zone excluded from body flow""]",backmatter_body,0.6,tail_nonref_hold_zone,unknown_like,none,True,True
4,8,text,Supplementary Material 2,"[122, 1123, 282, 1143]",backmatter_body,0.6,"[""default body_paragraph for text label"", ""tail_nonref_hold_zone excluded from body flow""]",backmatter_body,0.6,tail_nonref_hold_zone,unknown_like,none,True,True
4,9,text,Supplementary Material 3,"[122, 1153, 282, 1174]",backmatter_body,0.6,"[""default body_paragraph for text label"", ""tail_nonref_hold_zone excluded from body flow""]",backmatter_body,0.6,tail_nonref_hold_zone,unknown_like,none,True,True
4,10,paragraph_title,Acknowledgements,"[110, 1208, 245, 1223]",backmatter_heading,0.5,"[""backmatter heading on page 4: Acknowledgements""]",backmatter_heading_candidate,0.8,tail_nonref_hold_zone,unknown_like,short_fragment,True,True
4,11,text,Not applicable.,"[110, 1226, 205, 1243]",unknown_structural,0.3,"[""short text, uncertain role""]",unknown_structural,0.3,tail_nonref_hold_zone,unknown_like,short_fragment,False,True
4,12,paragraph_title,Author contributions,"[110, 1261, 253, 1278]",backmatter_heading,0.8,"[""backmatter heading on page 4: Author contributions""]",backmatter_heading_candidate,0.8,tail_nonref_hold_zone,support_like,none,True,True
4,13,text,J.-H. L. and S.-G.K. designed the study. S.-G.K. and H.-S.C. performed experiments. S.-G.K. and S.-Y.Y. analyzed the data and prepared graphs. S.-Y.Y. assisted in methods implementation. D.-H.R. acqui,"[107, 1279, 583, 1370]",backmatter_body,0.6,"[""default body_paragraph for text label"", ""tail_nonref_hold_zone excluded from body flow""]",backmatter_body,0.6,tail_nonref_hold_zone,unknown_like,none,True,True
4,14,paragraph_title,Funding,"[606, 165, 667, 183]",backmatter_heading,0.5,"[""backmatter heading on page 4: Funding""]",backmatter_heading_candidate,0.8,body_zone,body_like,short_fragment,True,True
4,15,text,This research was supported by The National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. NRF-2022R1F1A1073652 and RS-2023-00262398).,"[604, 183, 1024, 239]",backmatter_body,0.6,"[""default body_paragraph for text label""]",body_paragraph,0.6,body_zone,body_like,none,True,True
4,16,paragraph_title,Data availability,"[606, 255, 718, 273]",backmatter_heading,0.5,"[""backmatter heading on page 4: Data availability""]",backmatter_heading_candidate,0.8,body_zone,body_like,short_fragment,True,True
4,17,text,All data supporting the findings are included and are available on request from the corresponding author.,"[604, 273, 1052, 311]",frontmatter_noise,0.6,"[""default body_paragraph for text label"", ""frontmatter_side_zone excluded from body flow""]",frontmatter_noise,0.6,frontmatter_side_zone,support_like,none,False,False
4,18,paragraph_title,Declarations Ethics approval and consent to participate,"[605, 336, 885, 393]",backmatter_heading,0.5,"[""backmatter boundary candidate: Declarations Ethics approval and consent to participate""]",backmatter_boundary_candidate,0.5,body_zone,body_like,none,True,True
4,19,footer,,"[605, 375, 885, 393]",noise,0.9,"[""footer label""]",noise,0.9,body_zone,body_like,empty,False,False
4,20,text,"This study was conducted according to the National Institutes of Health Guide for the Care and Use of Laboratory Animals (NIH publication No. 86–23, revised in 1985) and was approved by the Institutio","[604, 393, 1079, 466]",backmatter_body,0.6,"[""default body_paragraph for text label""]",body_paragraph,0.6,body_zone,body_like,none,True,True
4,21,paragraph_title,Consent for publication,"[606, 483, 764, 501]",subsection_heading,0.6,"[""unnumbered paragraph_title, inferred level subsection_heading: Consent for publication""]",subsection_heading,0.6,body_zone,body_like,none,True,True
4,22,text,Not applicable.,"[606, 503, 703, 520]",unknown_structural,0.3,"[""short text, uncertain role""]",unknown_structural,0.3,body_zone,body_like,short_fragment,False,True
4,23,paragraph_title,Competing interests,"[606, 537, 744, 556]",backmatter_heading,0.5,"[""backmatter heading on page 4: Competing interests""]",backmatter_heading_candidate,0.8,body_zone,body_like,short_fragment,True,True
4,24,text,The authors declare that they have no competing interests.,"[605, 556, 964, 574]",backmatter_body,0.6,"[""default body_paragraph for text label""]",body_paragraph,0.6,body_zone,body_like,none,True,True
4,25,text,Received: 1 July 2024 / Accepted: 28 July 2024,"[605, 595, 899, 615]",frontmatter_noise,0.6,"[""default body_paragraph for text label"", ""frontmatter_side_zone excluded from body flow""]",frontmatter_noise,0.6,frontmatter_side_zone,support_like,none,False,False
4,26,text,Published online: 02 August 2024,"[605, 618, 847, 639]",frontmatter_noise,0.6,"[""default body_paragraph for text label"", ""frontmatter_side_zone excluded from body flow""]",frontmatter_noise,0.6,frontmatter_side_zone,support_like,none,False,False
4,27,paragraph_title,References,"[606, 687, 684, 706]",reference_heading,0.9,"[""references heading: References""]",reference_heading,0.9,reference_zone,unknown_like,short_fragment,True,True
4,28,reference_content,"1. Baulieu EE, Neurosteroids. A novel function of the brain. Psychoneuroendocrino. 1998;23(8):963–87.","[606, 706, 1069, 741]",reference_item,0.85,"[""reference content label: 1. Baulieu EE, Neurosteroids. A novel function of the brain.""]",reference_item,0.85,reference_zone,reference_like,reference_numeric_dot,True,True
4,29,reference_content,"2. Dubrovsky B. Neurosteroids, neuroactive steroids, and symptoms of affective disorders. Pharmacol Biochem Be. 2006;84(4):644–55.","[606, 743, 1073, 778]",reference_item,0.85,"[""reference content label: 2. Dubrovsky B. Neurosteroids, neuroactive steroids, and sym""]",reference_item,0.85,reference_zone,reference_like,reference_numeric_dot,True,True
4,30,reference_content,"3. Choi SR, Beitz AJ, Lee JH. Inhibition of cytochrome P450 side-chain cleavage attenuates the development of mechanical Allodynia by reducing spinal D-Serine production in a Murine Model of Neuropath","[606, 779, 1070, 848]",reference_item,0.85,"[""reference content label: 3. Choi SR, Beitz AJ, Lee JH. Inhibition of cytochrome P450 ""]",reference_item,0.85,reference_zone,reference_like,reference_numeric_dot,True,True
4,31,reference_content,"4. Lang PM, Schober GM, Rolke R, Wagner S, Hilge R, Offenbächer M, et al. Sensory neuropathy and signs of central sensitization in patients with peripheral arterial disease. Pain. 2006;124(1–2):190–20","[605, 851, 1075, 903]",reference_item,0.85,"[""reference content label: 4. Lang PM, Schober GM, Rolke R, Wagner S, Hilge R, Offenb\u00e4c""]",reference_item,0.85,reference_zone,reference_like,reference_numeric_dot,True,True
4,32,reference_content,"5. Seo HS, Kim HW, Roh DH, Yoon SY, Kwon YB, Han HJ, et al. A new rat model for thrombus-induced ischemic pain (TIIP); development of bilateral mechanical allodynia. Pain. 2008;139(3):520–32.","[606, 905, 1078, 957]",reference_item,0.85,"[""reference content label: 5. Seo HS, Kim HW, Roh DH, Yoon SY, Kwon YB, Han HJ, et al. ""]",reference_item,0.85,reference_zone,reference_like,reference_numeric_dot,True,True
4,33,reference_content,"6. Kwon SG, Roh DH, Yoon SY, Choi SR, Choi HS, Moon JY, et al. Role of peripheral sigma-1 receptors in ischaemic pain: potential interactions with ASIC and P2X receptors. Eur J Pain. 2016;20(4):594–60","[606, 959, 1076, 1011]",reference_item,0.85,"[""reference content label: 6. Kwon SG, Roh DH, Yoon SY, Choi SR, Choi HS, Moon JY, et a""]",reference_item,0.85,reference_zone,reference_like,reference_numeric_dot,True,True
4,34,reference_content,"7. Borowicz KK, Piskorska B, Banach M, Czuczwar SJ. Neuroprotective actions of neurosteroids. Front Endocrinol (Lausanne). 2011;2:50.","[606, 1013, 1074, 1048]",reference_item,0.85,"[""reference content label: 7. Borowicz KK, Piskorska B, Banach M, Czuczwar SJ. Neuropro""]",reference_item,0.85,reference_zone,reference_like,reference_numeric_dot,True,True
4,35,reference_content,"8. Slominski AT, Manna PR, Tuckey RC. On the role of skin in the regulation of local and systemic steroidogenic activities. Steroids. 2015;103:72–88.","[607, 1049, 1061, 1084]",reference_item,0.85,"[""reference content label: 8. Slominski AT, Manna PR, Tuckey RC. On the role of skin in""]",reference_item,0.85,reference_zone,reference_like,reference_numeric_dot,True,True
4,36,reference_content,"9. Shimada-Omori R, Yamasaki K, Koike S, Yamauchi T, Aiba S. TLR3 augments glucocorticoid-synthetic enzymes expression in epidermal keratinocytes; implications of glucocorticoid metabolism in rosacea ","[608, 1086, 1067, 1154]",reference_item,0.85,"[""reference content label: 9. Shimada-Omori R, Yamasaki K, Koike S, Yamauchi T, Aiba S.""]",reference_item,0.85,reference_zone,reference_like,reference_numeric_dot,True,True
4,37,reference_content,"10. Maurice T. Neurosteroids and $ \sigma $ receptors, biochemical and behavioral relevance. Pharmacopsychiatry. 2004;37:S171–82.","[608, 1157, 1038, 1192]",reference_item,0.85,"[""reference content label: 10. Maurice T. Neurosteroids and $ \\sigma $ receptors, bioc""]",reference_item,0.85,reference_zone,reference_like,reference_numeric_dot,True,True
4,38,reference_content,"11. Kwon SG, Yoon SY, Roh DH, Choi SR, Choi HS, Moon JY, et al. Peripheral neurosteroids enhance P2X receptor-induced mechanical allodynia via a sigma-1 receptor-mediated mechanism. Brain Res Bull. 20","[608, 1194, 1053, 1247]",reference_item,0.85,"[""reference content label: 11. Kwon SG, Yoon SY, Roh DH, Choi SR, Choi HS, Moon JY, et ""]",reference_item,0.85,reference_zone,reference_like,reference_numeric_dot,True,True
4,39,paragraph_title,Publisher's Note,"[606, 1279, 761, 1299]",backmatter_heading,0.5,"[""backmatter heading on page 4: Publisher's Note""]",backmatter_heading_candidate,0.8,tail_nonref_hold_zone,support_like,short_fragment,True,True
4,40,text,Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.,"[605, 1300, 1026, 1339]",backmatter_body,0.6,"[""default body_paragraph for text label""]",body_paragraph,0.6,tail_nonref_hold_zone,unknown_like,none,True,True
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2 1 0 header Kwon et al. Molecular Brain (2024) 17:50 https://doi.org/10.1186/s13041-024-01125-2 [109, 66, 414, 108] noise 0.9 ["header label"] noise 0.9 body_zone body_like none False False
3 1 1 header Molecular Brain [912, 65, 1081, 94] noise 0.9 ["header label"] noise 0.9 frontmatter_main_zone support_like short_fragment False False
4 1 2 text MICRO REPORT [116, 182, 335, 211] authors 0.6 ["page-1 initial-lastname author byline: MICRO REPORT"] authors 0.6 frontmatter_main_zone support_like short_fragment True True
5 1 3 text Open Access [922, 183, 1075, 212] frontmatter_noise 0.7 ["frontmatter noise text: Open Access"] frontmatter_noise 0.7 frontmatter_main_zone support_like short_fragment False False
6 1 4 doc_title Intraplantar aminoglutethimide, a P450scc inhibitor, reduced the induction of mechanical allodynia in a rat model of thrombus-induced ischemic pain [107, 246, 995, 453] paper_title 0.8 ["page-1 zone title_zone: Intraplantar aminoglutethimide, a P450scc inhibitor, reduced"] paper_title 0.8 frontmatter_main_zone support_like none True True
7 1 5 image [1019, 226, 1077, 283] media_asset 0.85 ["media label: image"] media_asset 0.85 frontmatter_main_zone support_like empty True True
8 1 6 text Soon-Gu Kwon $ ^{1†} $, Hoon-Seong Choi $ ^{2†} $, Seo-Yeon Yoon $ ^{3} $, Dae-Hyun Roh $ ^{4*} $ $ ^{ID} $ and Jang-Hern Lee $ ^{5*} $ [107, 489, 906, 518] authors 0.8 ["page-1 zone author_zone: Soon-Gu Kwon $ ^{1\u2020} $, Hoon-Seong Choi $ ^{2\u2020} $, Seo-Yeon "] authors 0.8 frontmatter_main_zone support_like none True True
9 1 7 paragraph_title Abstract [121, 570, 209, 593] abstract_heading 0.95 ["abstract heading"] abstract_heading 0.95 frontmatter_main_zone heading_like short_fragment True True
10 1 8 abstract Neuroactive steroids (NASs) directly affect neuronal excitability. Despite their role in the nervous system is intimately linked to pain control, knowledge is currently limited. This study investigate [120, 593, 1061, 842] abstract_body 0.85 ["abstract label from Paddle OCR"] abstract_body 0.85 frontmatter_main_zone support_like none True True
11 1 9 text Keywords Ischemic pain, Neuroactive steroid, Aminoglutethimide, Cytochrome P450 side-chain cleavage, Sigma-1 receptor [120, 840, 1048, 890] structured_insert 0.7 ["keyword-like block: Keywords Ischemic pain, Neuroactive steroid, Aminoglutethimi"] frontmatter_noise 0.7 frontmatter_main_zone support_like none False False
12 1 10 footnote $ ^{\dagger} $Soon-Gu Kwon and Hoon-Seong Choi contributed equally to this work. [109, 971, 513, 1009] frontmatter_support 0.78 ["first-surviving-page support text: $ ^{\\dagger} $Soon-Gu Kwon and Hoon-Seong Choi contributed e"] frontmatter_support 0.78 body_zone body_like affiliation_marker True True
13 1 11 paragraph_title Main text [605, 941, 702, 964] section_heading 0.5 ["unnumbered paragraph_title on page 1 outside title zone: Main text"] section_heading 0.5 body_zone heading_like short_fragment True True
14 1 12 footnote *Correspondence: Dae-Hyun Roh dhroh@khu.ac.kr Jang-Hern Lee jhl1101@snu.ac.kr Neuracle Science Co., Ltd, Seoul 02841, Korea Research Animal Resource Center, Korea Institute of Science and Technology [106, 1008, 582, 1304] footnote 0.7 ["footnote label: *Correspondence:\nDae-Hyun Roh\ndhroh@khu.ac.kr\nJang-Hern Lee\n"] footnote 0.7 body_zone body_like none True True
15 1 13 text Neuroactive steroids (NASs) are a class of steroids that can modify neuronal excitability in both the central and peripheral nervous systems [1, 2]. The initial rate-limiting step in NAS biosynthesis [603, 966, 1083, 1279] body_paragraph 0.6 ["default body_paragraph for text label"] body_paragraph 0.6 body_zone body_like none True True
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17 1 15 footnote © The Author(s) 2024. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any [355, 1344, 1082, 1473] frontmatter_noise 0.8 ["page-1 zone journal_furniture_zone: \u00a9 The Author(s) 2024. Open Access This article is licensed u"] frontmatter_noise 0.8 body_zone support_like none False False
18 2 0 header Kwon et al. Molecular Brain (2024) 17:50 [109, 66, 428, 87] noise 0.9 ["header label"] noise 0.9 unknown_like none False False
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31 2 13 figure_title Fig. 1 (See legend on next page.) [110, 1292, 332, 1315] figure_caption 0.92 ["figure_title label: Fig. 1 (See legend on next page.)"] figure_caption 0.92 display_zone legend_like figure_number True True
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33 2 15 text Peripheral ischemia is common causes of pain in the lower extremities [4]. Previously, we developed a peripheral ischemic pain model named the “thrombus-induced ischemic pain (TIIP) model,” which mimi [106, 1351, 586, 1474] body_paragraph 0.6 ["default body_paragraph for text label"] body_paragraph 0.6 body_zone body_like none True True
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37 2 19 text this model, we focused on the pain mechanism in context of peripheral sensitization and examined related receptors, such as acid-sensing ion channels, ATP receptors, and sigma-1 receptors (Sig-1Rs), w [602, 1351, 1084, 1474] body_paragraph 0.6 ["default body_paragraph for text label"] body_paragraph 0.6 body_zone body_like none True True
38 3 0 header Kwon et al. Molecular Brain (2024) 17:50 [110, 67, 426, 87] noise 0.9 ["header label"] noise 0.9 unknown_like none False False
39 3 1 number Page 3 of 4 [998, 67, 1081, 87] noise 0.9 ["page number label"] noise 0.9 body_zone body_like short_fragment False False
40 3 2 text (See figure on previous page.) [109, 166, 307, 185] body_paragraph 0.6 ["default body_paragraph for text label"] body_paragraph 0.6 body_zone body_like none True True
41 3 3 figure_title Fig. 1 Schematic diagram for surgery of thrombus-induced ischemic pain (TIIP) injury and the effect of intraplantar aminoglutethimide (AMG) and PRE-084 (PRE) on TIIP induced mechanical allodynia. (A) [109, 184, 1082, 547] figure_caption 0.92 ["figure_title label: Fig. 1 Schematic diagram for surgery of thrombus-induced isc"] figure_caption 0.92 display_zone legend_like figure_number True True
42 3 4 text study, based on the fact that NASs have been reported to control ischemic damage in the nervous system [7], we hypothesized that intraplantar injection of P450scc inhibitor could modulate ischemic pai [107, 582, 585, 774] body_paragraph 0.6 ["default body_paragraph for text label"] body_paragraph 0.6 body_zone body_like none True True
43 3 5 text Male Sprague–Dawley rats (250–350 g) were anesthetized with 3% isoflurane in a N₂O/O₂ gas mixture, and small incision was made in the femoral triangle of the left hind limb. Femoral artery was careful [107, 776, 585, 1375] body_paragraph 0.6 ["default body_paragraph for text label"] body_paragraph 0.6 body_zone body_like none True True
44 3 6 text The temporal expression level of P450scc on the ipsilateral skin exhibited a gradual increase following ischemia. Immunoblot analysis revealed that P450scc levels peaked on post-operative day 7. Altho [107, 1376, 586, 1473] body_paragraph 0.6 ["default body_paragraph for text label"] body_paragraph 0.6 body_zone body_like none True True
45 3 7 text [602, 582, 1083, 965] unknown_structural 0.3 ["short text, uncertain role"] unknown_structural 0.3 body_zone body_like empty False True
46 3 8 text To investigate the role of peripheral Sig-1Rs in AMG-induced pain inhibition in induction phase, PRE (10 nmol) was co-administered with AMG (100 nmol) during the induction phase. Co-administration of [603, 967, 1083, 1182] body_paragraph 0.6 ["default body_paragraph for text label"] body_paragraph 0.6 body_zone body_like none True True
47 3 9 text Skin is one of the main extra-gonadal steroidogenic organs that contains the P450scc enzyme [8]. Previous studies have shown that P450scc expression in skin can be regulated by external irritants [9]. [602, 1183, 1083, 1450] body_paragraph 0.6 ["default body_paragraph for text label"] body_paragraph 0.6 body_zone body_like none True True
48 4 0 header Kwon et al. Molecular Brain (2024) 17:50 [109, 66, 427, 87] noise 0.9 ["header label"] noise 0.9 unknown_like none False False
49 4 1 number Page 4 of 4 [998, 67, 1082, 87] noise 0.9 ["page number label"] noise 0.9 body_zone body_like short_fragment False False
50 4 2 text that provides protection against nerve damage resulting from ischemia. [107, 175, 585, 221] body_paragraph 0.6 ["default body_paragraph for text label"] body_paragraph 0.6 body_zone body_like none True True
51 4 3 text Sig-1Rs are primary target receptors for NAS such as PREG and dehydroepiandrosterone (DHEA) [10]. We previously reported that peripherally administered PREG sulfate (PREGS) and DHEA sulfate (DHEAS) en [108, 223, 586, 798] body_paragraph 0.6 ["default body_paragraph for text label"] body_paragraph 0.6 tail_nonref_hold_zone body_like none True True
52 4 4 text Overall, our study demonstrated that peripheral P450scc level was increased in ischemic damage and intraplantar injection of AMG significantly alleviated mechanical allodynia during the induction of T [107, 798, 585, 992] body_paragraph 0.6 ["default body_paragraph for text label"] body_paragraph 0.6 tail_nonref_hold_zone body_like none True True
53 4 5 paragraph_title Supplementary Information [109, 1015, 372, 1037] backmatter_boundary_candidate 0.5 ["backmatter boundary candidate: Supplementary Information"] backmatter_boundary_candidate 0.5 tail_nonref_hold_zone heading_like none True True
54 4 6 text The online version contains supplementary material available at https://doi.org/10.1186/s13041-024-01125-2. [109, 1036, 566, 1074] backmatter_body 0.6 ["default body_paragraph for text label", "tail_nonref_hold_zone excluded from body flow"] backmatter_body 0.6 tail_nonref_hold_zone unknown_like none True True
55 4 7 text Supplementary Material 1 [121, 1094, 281, 1114] backmatter_body 0.6 ["default body_paragraph for text label", "tail_nonref_hold_zone excluded from body flow"] backmatter_body 0.6 tail_nonref_hold_zone unknown_like none True True
56 4 8 text Supplementary Material 2 [122, 1123, 282, 1143] backmatter_body 0.6 ["default body_paragraph for text label", "tail_nonref_hold_zone excluded from body flow"] backmatter_body 0.6 tail_nonref_hold_zone unknown_like none True True
57 4 9 text Supplementary Material 3 [122, 1153, 282, 1174] backmatter_body 0.6 ["default body_paragraph for text label", "tail_nonref_hold_zone excluded from body flow"] backmatter_body 0.6 tail_nonref_hold_zone unknown_like none True True
58 4 10 paragraph_title Acknowledgements [110, 1208, 245, 1223] backmatter_heading 0.5 ["backmatter heading on page 4: Acknowledgements"] backmatter_heading_candidate 0.8 tail_nonref_hold_zone unknown_like short_fragment True True
59 4 11 text Not applicable. [110, 1226, 205, 1243] unknown_structural 0.3 ["short text, uncertain role"] unknown_structural 0.3 tail_nonref_hold_zone unknown_like short_fragment False True
60 4 12 paragraph_title Author contributions [110, 1261, 253, 1278] backmatter_heading 0.8 ["backmatter heading on page 4: Author contributions"] backmatter_heading_candidate 0.8 tail_nonref_hold_zone support_like none True True
61 4 13 text J.-H. L. and S.-G.K. designed the study. S.-G.K. and H.-S.C. performed experiments. S.-G.K. and S.-Y.Y. analyzed the data and prepared graphs. S.-Y.Y. assisted in methods implementation. D.-H.R. acqui [107, 1279, 583, 1370] backmatter_body 0.6 ["default body_paragraph for text label", "tail_nonref_hold_zone excluded from body flow"] backmatter_body 0.6 tail_nonref_hold_zone unknown_like none True True
62 4 14 paragraph_title Funding [606, 165, 667, 183] backmatter_heading 0.5 ["backmatter heading on page 4: Funding"] backmatter_heading_candidate 0.8 body_zone body_like short_fragment True True
63 4 15 text This research was supported by The National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. NRF-2022R1F1A1073652 and RS-2023-00262398). [604, 183, 1024, 239] backmatter_body 0.6 ["default body_paragraph for text label"] body_paragraph 0.6 body_zone body_like none True True
64 4 16 paragraph_title Data availability [606, 255, 718, 273] backmatter_heading 0.5 ["backmatter heading on page 4: Data availability"] backmatter_heading_candidate 0.8 body_zone body_like short_fragment True True
65 4 17 text All data supporting the findings are included and are available on request from the corresponding author. [604, 273, 1052, 311] frontmatter_noise 0.6 ["default body_paragraph for text label", "frontmatter_side_zone excluded from body flow"] frontmatter_noise 0.6 frontmatter_side_zone support_like none False False
66 4 18 paragraph_title Declarations Ethics approval and consent to participate [605, 336, 885, 393] backmatter_heading 0.5 ["backmatter boundary candidate: Declarations Ethics approval and consent to participate"] backmatter_boundary_candidate 0.5 body_zone body_like none True True
67 4 19 footer [605, 375, 885, 393] noise 0.9 ["footer label"] noise 0.9 body_zone body_like empty False False
68 4 20 text This study was conducted according to the National Institutes of Health Guide for the Care and Use of Laboratory Animals (NIH publication No. 86–23, revised in 1985) and was approved by the Institutio [604, 393, 1079, 466] backmatter_body 0.6 ["default body_paragraph for text label"] body_paragraph 0.6 body_zone body_like none True True
69 4 21 paragraph_title Consent for publication [606, 483, 764, 501] subsection_heading 0.6 ["unnumbered paragraph_title, inferred level subsection_heading: Consent for publication"] subsection_heading 0.6 body_zone body_like none True True
70 4 22 text Not applicable. [606, 503, 703, 520] unknown_structural 0.3 ["short text, uncertain role"] unknown_structural 0.3 body_zone body_like short_fragment False True
71 4 23 paragraph_title Competing interests [606, 537, 744, 556] backmatter_heading 0.5 ["backmatter heading on page 4: Competing interests"] backmatter_heading_candidate 0.8 body_zone body_like short_fragment True True
72 4 24 text The authors declare that they have no competing interests. [605, 556, 964, 574] backmatter_body 0.6 ["default body_paragraph for text label"] body_paragraph 0.6 body_zone body_like none True True
73 4 25 text Received: 1 July 2024 / Accepted: 28 July 2024 [605, 595, 899, 615] frontmatter_noise 0.6 ["default body_paragraph for text label", "frontmatter_side_zone excluded from body flow"] frontmatter_noise 0.6 frontmatter_side_zone support_like none False False
74 4 26 text Published online: 02 August 2024 [605, 618, 847, 639] frontmatter_noise 0.6 ["default body_paragraph for text label", "frontmatter_side_zone excluded from body flow"] frontmatter_noise 0.6 frontmatter_side_zone support_like none False False
75 4 27 paragraph_title References [606, 687, 684, 706] reference_heading 0.9 ["references heading: References"] reference_heading 0.9 reference_zone unknown_like short_fragment True True
76 4 28 reference_content 1. Baulieu EE, Neurosteroids. A novel function of the brain. Psychoneuroendocrino. 1998;23(8):963–87. [606, 706, 1069, 741] reference_item 0.85 ["reference content label: 1. Baulieu EE, Neurosteroids. A novel function of the brain."] reference_item 0.85 reference_zone reference_like reference_numeric_dot True True
77 4 29 reference_content 2. Dubrovsky B. Neurosteroids, neuroactive steroids, and symptoms of affective disorders. Pharmacol Biochem Be. 2006;84(4):644–55. [606, 743, 1073, 778] reference_item 0.85 ["reference content label: 2. Dubrovsky B. Neurosteroids, neuroactive steroids, and sym"] reference_item 0.85 reference_zone reference_like reference_numeric_dot True True
78 4 30 reference_content 3. Choi SR, Beitz AJ, Lee JH. Inhibition of cytochrome P450 side-chain cleavage attenuates the development of mechanical Allodynia by reducing spinal D-Serine production in a Murine Model of Neuropath [606, 779, 1070, 848] reference_item 0.85 ["reference content label: 3. Choi SR, Beitz AJ, Lee JH. Inhibition of cytochrome P450 "] reference_item 0.85 reference_zone reference_like reference_numeric_dot True True
79 4 31 reference_content 4. Lang PM, Schober GM, Rolke R, Wagner S, Hilge R, Offenbächer M, et al. Sensory neuropathy and signs of central sensitization in patients with peripheral arterial disease. Pain. 2006;124(1–2):190–20 [605, 851, 1075, 903] reference_item 0.85 ["reference content label: 4. Lang PM, Schober GM, Rolke R, Wagner S, Hilge R, Offenb\u00e4c"] reference_item 0.85 reference_zone reference_like reference_numeric_dot True True
80 4 32 reference_content 5. Seo HS, Kim HW, Roh DH, Yoon SY, Kwon YB, Han HJ, et al. A new rat model for thrombus-induced ischemic pain (TIIP); development of bilateral mechanical allodynia. Pain. 2008;139(3):520–32. [606, 905, 1078, 957] reference_item 0.85 ["reference content label: 5. Seo HS, Kim HW, Roh DH, Yoon SY, Kwon YB, Han HJ, et al. "] reference_item 0.85 reference_zone reference_like reference_numeric_dot True True
81 4 33 reference_content 6. Kwon SG, Roh DH, Yoon SY, Choi SR, Choi HS, Moon JY, et al. Role of peripheral sigma-1 receptors in ischaemic pain: potential interactions with ASIC and P2X receptors. Eur J Pain. 2016;20(4):594–60 [606, 959, 1076, 1011] reference_item 0.85 ["reference content label: 6. Kwon SG, Roh DH, Yoon SY, Choi SR, Choi HS, Moon JY, et a"] reference_item 0.85 reference_zone reference_like reference_numeric_dot True True
82 4 34 reference_content 7. Borowicz KK, Piskorska B, Banach M, Czuczwar SJ. Neuroprotective actions of neurosteroids. Front Endocrinol (Lausanne). 2011;2:50. [606, 1013, 1074, 1048] reference_item 0.85 ["reference content label: 7. Borowicz KK, Piskorska B, Banach M, Czuczwar SJ. Neuropro"] reference_item 0.85 reference_zone reference_like reference_numeric_dot True True
83 4 35 reference_content 8. Slominski AT, Manna PR, Tuckey RC. On the role of skin in the regulation of local and systemic steroidogenic activities. Steroids. 2015;103:72–88. [607, 1049, 1061, 1084] reference_item 0.85 ["reference content label: 8. Slominski AT, Manna PR, Tuckey RC. On the role of skin in"] reference_item 0.85 reference_zone reference_like reference_numeric_dot True True
84 4 36 reference_content 9. Shimada-Omori R, Yamasaki K, Koike S, Yamauchi T, Aiba S. TLR3 augments glucocorticoid-synthetic enzymes expression in epidermal keratinocytes; implications of glucocorticoid metabolism in rosacea [608, 1086, 1067, 1154] reference_item 0.85 ["reference content label: 9. Shimada-Omori R, Yamasaki K, Koike S, Yamauchi T, Aiba S."] reference_item 0.85 reference_zone reference_like reference_numeric_dot True True
85 4 37 reference_content 10. Maurice T. Neurosteroids and $ \sigma $ receptors, biochemical and behavioral relevance. Pharmacopsychiatry. 2004;37:S171–82. [608, 1157, 1038, 1192] reference_item 0.85 ["reference content label: 10. Maurice T. Neurosteroids and $ \\sigma $ receptors, bioc"] reference_item 0.85 reference_zone reference_like reference_numeric_dot True True
86 4 38 reference_content 11. Kwon SG, Yoon SY, Roh DH, Choi SR, Choi HS, Moon JY, et al. Peripheral neurosteroids enhance P2X receptor-induced mechanical allodynia via a sigma-1 receptor-mediated mechanism. Brain Res Bull. 20 [608, 1194, 1053, 1247] reference_item 0.85 ["reference content label: 11. Kwon SG, Yoon SY, Roh DH, Choi SR, Choi HS, Moon JY, et "] reference_item 0.85 reference_zone reference_like reference_numeric_dot True True
87 4 39 paragraph_title Publisher's Note [606, 1279, 761, 1299] backmatter_heading 0.5 ["backmatter heading on page 4: Publisher's Note"] backmatter_heading_candidate 0.8 tail_nonref_hold_zone support_like short_fragment True True
88 4 40 text Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. [605, 1300, 1026, 1339] backmatter_body 0.6 ["default body_paragraph for text label"] body_paragraph 0.6 tail_nonref_hold_zone unknown_like none True True

70
tests/fixtures/ocr_real_papers/DWQQK2YB/block_trace.csv vendored
View file

@ -1,13 +1,13 @@
page,block_id,raw_label,content_preview,bbox,role,role_confidence,evidence,seed_role,seed_confidence,zone,style_family,marker_type,render_default,index_default
2,0,header,Journal Pre-proof,"[502, 1, 832, 40]",frontmatter_noise,0.98,"[""journal pre-proof running header suppressed: p2 y=1/1584""]",frontmatter_noise,0.98,preproof_cover_zone,unknown_like,preproof_marker,False,False
2,1,text,TITLE,"[76, 101, 146, 128]",unknown_structural,0.3,"[""short text, uncertain role""]",unknown_structural,0.3,frontmatter_side_zone,support_like,short_fragment,False,True
2,0,header,Journal Pre-proof,"[502, 1, 832, 40]",frontmatter_noise,0.98,"[""journal pre-proof running header suppressed: p2 y=1/1584"", ""page_1_preproof_cover_dropped_upstream""]",frontmatter_noise,0.98,preproof_cover_zone,unknown_like,preproof_marker,False,False
2,1,text,TITLE,"[76, 101, 146, 128]",unknown_structural,0.3,"[""short text, uncertain role""]",unknown_structural,0.3,frontmatter_main_zone,support_like,short_fragment,False,True
2,2,doc_title,"Magnetoresponsive Stem Cell Spheroid-based Cartilage Recovery
Platform Utilizing Electromagnetic Fields","[74, 155, 1028, 268]",unknown_structural,0.2,"[""unrecognized label 'doc_title'""]",unknown_structural,0.2,body_zone,body_like,none,False,True
2,3,text,"Ami Yoo<sup>a</sup>, †, Gwangjun Go<sup>a</sup>, <sup>b</sup>, †, Kim Tien Nguyen<sup>a</sup>, <sup>b</sup>, Kyungmin Lee<sup>a</sup>, <sup>b</sup>, Hyun-Ki Min<sup>a</sup>, Byungjeon Kang<sup>a</sup>","[74, 355, 1026, 445]",frontmatter_support,0.6,"[""default body_paragraph for text label""]",body_paragraph,0.6,body_zone,body_like,none,True,True
2,4,text,"a. Korea Institute of Medical Microrobotics, 43-26 Cheomdangwagi-ro, Buk-gu, Gwangju, 61011, Korea","[82, 535, 1028, 621]",frontmatter_support,0.6,"[""default body_paragraph for text label""]",body_paragraph,0.6,body_zone,body_like,none,True,True
2,5,text,"b. School of Mechanical Engineering, Chonnam National University, 77 Yongbong-ro, Buk-gu, Gwangju, 61186, Korea","[81, 645, 1028, 732]",frontmatter_support,0.6,"[""default body_paragraph for text label""]",body_paragraph,0.6,body_zone,body_like,none,True,True
2,6,text,These authors contributed equally to this work.,"[75, 811, 548, 843]",frontmatter_noise,0.6,"[""default body_paragraph for text label"", ""frontmatter_side_zone excluded from body flow""]",frontmatter_noise,0.6,frontmatter_side_zone,support_like,none,False,False
2,7,text,"*Corresponding author: judyvet@jnu.ac.kr (J.H.), jop@jnu.ac.kr (J.-O.P.), and eunpyochoi@jnu.ac.kr (E.C.)","[74, 864, 1032, 953]",frontmatter_noise,0.6,"[""default body_paragraph for text label"", ""frontmatter_side_zone excluded from body flow""]",frontmatter_noise,0.6,frontmatter_side_zone,support_like,none,False,False
Platform Utilizing Electromagnetic Fields","[74, 155, 1028, 268]",paper_title,0.8,"[""page-1 zone title_zone: Magnetoresponsive Stem Cell Spheroid-based Cartilage Recover""]",paper_title,0.8,frontmatter_main_zone,support_like,none,True,True
2,3,text,"Ami Yoo<sup>a</sup>, †, Gwangjun Go<sup>a</sup>, <sup>b</sup>, †, Kim Tien Nguyen<sup>a</sup>, <sup>b</sup>, Kyungmin Lee<sup>a</sup>, <sup>b</sup>, Hyun-Ki Min<sup>a</sup>, Byungjeon Kang<sup>a</sup>","[74, 355, 1026, 445]",authors,0.8,"[""page-1 zone author_zone: Ami Yoo<sup>a</sup>, \u2020, Gwangjun Go<sup>a</sup>, <sup>b</sup""]",authors,0.8,frontmatter_main_zone,support_like,none,True,True
2,4,text,"a. Korea Institute of Medical Microrobotics, 43-26 Cheomdangwagi-ro, Buk-gu, Gwangju, 61011, Korea","[82, 535, 1028, 621]",unknown_structural,0.85,"[""page-1 title fallback after pre-proof: y=535/1584""]",paper_title,0.85,frontmatter_main_zone,support_like,none,False,True
2,5,text,"b. School of Mechanical Engineering, Chonnam National University, 77 Yongbong-ro, Buk-gu, Gwangju, 61186, Korea","[81, 645, 1028, 732]",affiliation,0.8,"[""page-1 zone affiliation_zone: b. School of Mechanical Engineering, Chonnam National Univer""]",affiliation,0.8,frontmatter_main_zone,support_like,none,True,True
2,6,text,These authors contributed equally to this work.,"[75, 811, 548, 843]",frontmatter_support,0.78,"[""first-surviving-page support text: These authors contributed equally to this work.""]",frontmatter_support,0.78,frontmatter_main_zone,support_like,none,True,True
2,7,text,"*Corresponding author: judyvet@jnu.ac.kr (J.H.), jop@jnu.ac.kr (J.-O.P.), and eunpyochoi@jnu.ac.kr (E.C.)","[74, 864, 1032, 953]",frontmatter_support,0.78,"[""first-surviving-page support text: *Corresponding author: judyvet@jnu.ac.kr (J.H.), jop@jnu.ac.""]",frontmatter_support,0.78,frontmatter_main_zone,support_like,none,True,True
2,8,paragraph_title,Highlights,"[75, 1115, 188, 1143]",structured_insert,0.7,"[""structured insert label: highlights""]",structured_insert_candidate,0.7,body_zone,heading_like,short_fragment,False,False
2,9,text,- We propose the use of a magnetoresponsive stem cell spheroid (MR-SCS)-based cartilage recovery platform that allows for precise targeting using an electromagnetic actuation system to provide magneti,"[109, 1144, 1014, 1253]",structured_insert,0.6,"[""default body_paragraph for text label""]",body_paragraph,0.6,body_zone,body_like,none,False,False
2,10,text,- Locomotion of MR-SCS that was mediated by the electromagnetic actuationsystem was successfully demonstrated in 3D phantom and ex vivomodels.,"[111, 1278, 966, 1332]",structured_insert,0.6,"[""default body_paragraph for text label""]",body_paragraph,0.6,body_zone,body_like,none,False,False
@ -107,11 +107,11 @@ Platform Utilizing Electromagnetic Fields","[74, 155, 1028, 268]",unknown_struct
24,2,paragraph_title,4. Conclusion,"[76, 986, 247, 1016]",section_heading,0.85,"[""paragraph_title label with numbering: 4. Conclusion""]",section_heading,0.85,body_zone,heading_like,heading_numbered,True,True
24,3,text,We developed a magnetoresponsive stem cell spheroid-based cartilage recovery platform that can precisely deliver stem cells using an EMA system and can stimulate these cells by LF-EMF to promote chond,"[75, 1050, 1033, 1411]",body_paragraph,0.6,"[""default body_paragraph for text label""]",body_paragraph,0.6,body_zone,body_like,none,True,True
25,0,header,Journal Pre-proof,"[504, 2, 830, 39]",frontmatter_noise,0.98,"[""journal pre-proof running header suppressed: p25 y=2/1584""]",frontmatter_noise,0.98,preproof_cover_zone,unknown_like,preproof_marker,False,False
25,1,text,fields can provide effective potential therapeutic agents to promote the regeneration of damaged tissue within articular cartilage.,"[75, 100, 1031, 186]",body_paragraph,0.6,"[""default body_paragraph for text label"", ""tail_nonref_hold_zone excluded from body flow"", ""tail_nonref_hold_zone excluded from body flow""]",backmatter_body,0.6,tail_nonref_hold_zone,unknown_like,none,True,True
25,1,text,fields can provide effective potential therapeutic agents to promote the regeneration of damaged tissue within articular cartilage.,"[75, 100, 1031, 186]",body_paragraph,0.6,"[""default body_paragraph for text label""]",body_paragraph,0.6,tail_nonref_hold_zone,unknown_like,none,True,True
25,2,paragraph_title,Conflict of Interest,"[76, 241, 311, 270]",backmatter_heading,0.8,"[""backmatter heading on page 25: Conflict of Interest""]",backmatter_heading_candidate,0.8,tail_nonref_hold_zone,support_like,none,True,True
25,3,text,All other authors declare that they have no competing interests.,"[75, 359, 690, 387]",backmatter_body,0.6,"[""default body_paragraph for text label"", ""tail_nonref_hold_zone excluded from body flow""]",backmatter_body,0.6,tail_nonref_hold_zone,unknown_like,none,True,True
25,3,text,All other authors declare that they have no competing interests.,"[75, 359, 690, 387]",backmatter_body,0.6,"[""default body_paragraph for text label""]",body_paragraph,0.6,tail_nonref_hold_zone,unknown_like,none,True,True
25,4,paragraph_title,Acknowledgments,"[76, 444, 304, 474]",backmatter_heading,0.8,"[""backmatter heading on page 25: Acknowledgments""]",backmatter_heading_candidate,0.8,tail_nonref_hold_zone,heading_like,short_fragment,True,True
25,5,text,A. Yoo and G. Go contributed equally to this work. We thank Medical Microrobot Center of Chonnam National University for supporting the equipment. This research was supported by a grant of the Korea H,"[74, 518, 1032, 881]",backmatter_body,0.6,"[""default body_paragraph for text label"", ""tail_nonref_hold_zone excluded from body flow""]",backmatter_body,0.6,tail_nonref_hold_zone,unknown_like,none,True,True
25,5,text,A. Yoo and G. Go contributed equally to this work. We thank Medical Microrobot Center of Chonnam National University for supporting the equipment. This research was supported by a grant of the Korea H,"[74, 518, 1032, 881]",backmatter_body,0.6,"[""default body_paragraph for text label""]",body_paragraph,0.6,tail_nonref_hold_zone,unknown_like,none,True,True
26,0,header,Journal Pre-proof,"[503, 2, 831, 39]",frontmatter_noise,0.98,"[""journal pre-proof running header suppressed: p26 y=2/1584""]",frontmatter_noise,0.98,preproof_cover_zone,unknown_like,preproof_marker,False,False
26,1,paragraph_title,References,"[75, 102, 216, 133]",reference_heading,0.9,"[""references heading: References""]",reference_heading,0.9,reference_zone,heading_like,short_fragment,True,True
26,2,reference_content,"[1] C. Cooper, M.K. Javaid, N. Arden, Epidemiology of osteoarthritis, Atlas of osteoarthritis, Springer 2014, pp. 21-36.","[78, 166, 1027, 249]",reference_item,0.85,"[""reference content label: [1] C. Cooper, M.K. Javaid, N. Arden, Epidemiology of osteoa""]",reference_item,0.85,reference_zone,reference_like,reference_numeric_bracket,True,True
@ -178,7 +178,7 @@ Platform Utilizing Electromagnetic Fields","[74, 155, 1028, 268]",unknown_struct
31,10,reference_content,"[57] F. Torossian, A. Bisson, J.-P. Vannier, O. Boyer, M. Lamacz, TRPC expression in mesenchymal stem cells2010.","[79, 1369, 1028, 1452]",reference_item,0.85,"[""reference content label: [57] F. Torossian, A. Bisson, J.-P. Vannier, O. Boyer, M. La""]",reference_item,0.85,reference_zone,reference_like,reference_numeric_bracket,True,True
32,0,header,Journal Pre-proof,"[503, 2, 831, 40]",frontmatter_noise,0.98,"[""journal pre-proof running header suppressed: p32 y=2/1584""]",frontmatter_noise,0.98,preproof_cover_zone,unknown_like,preproof_marker,False,False
32,1,reference_content,"[58] R.G. LeBaron, K.A. Athanasiou, Ex vivo synthesis of articular cartilage, Biomaterials, 21(2000) 2575-87.","[77, 99, 1028, 183]",backmatter_body,0.85,"[""reference content label: [58] R.G. LeBaron, K.A. Athanasiou, Ex vivo synthesis of art""]",reference_item,0.85,post_reference_backmatter_zone,reference_like,reference_numeric_bracket,True,True
32,2,paragraph_title,Biographies,"[81, 350, 234, 380]",backmatter_heading,0.6,"[""unnumbered paragraph_title, inferred level sub_subsection_heading: Biographies""]",sub_subsection_heading,0.6,post_reference_backmatter_zone,heading_like,short_fragment,True,True
32,2,paragraph_title,Biographies,"[81, 350, 234, 380]",sub_subsection_heading,0.6,"[""unnumbered paragraph_title, inferred level sub_subsection_heading: Biographies""]",sub_subsection_heading,0.6,post_reference_backmatter_zone,heading_like,short_fragment,True,True
32,3,text,"Amr Yoo received her B.S. (2010) degrees from the Dep. of Applied Bioscience and Biotechnology at Chonnam National University, Korea and M.S. (2013) degrees from Division of Agriculture, Food, and Nat","[75, 382, 1025, 511]",backmatter_body,0.6,"[""default body_paragraph for text label""]",body_paragraph,0.6,post_reference_backmatter_zone,unknown_like,none,True,True
32,4,reference_content,"Gwangjun Go received his B.S. (2013) and M.S. (2015) degree from the School of Mechanical Engineering at Chonnam National University, Korea. Currently, he is a Ph.D. candidate in the School of Mechani","[75, 513, 1018, 619]",backmatter_body,0.85,"[""reference content label: Gwangjun Go received his B.S. (2013) and M.S. (2015) degree ""]",reference_item,0.85,post_reference_backmatter_zone,unknown_like,none,True,True
32,5,reference_content,"Kim Tien Nguyen received a B.S. degree in Dept. of Mechanical Engineering from the Hochiminh University of Technology and Education, Hochiminh, Vietnam, in 2012 and the M.S. degree in School of Mechan","[75, 620, 1021, 777]",backmatter_body,0.85,"[""reference content label: Kim Tien Nguyen received a B.S. degree in Dept. of Mechanica""]",reference_item,0.85,post_reference_backmatter_zone,unknown_like,none,True,True
@ -195,19 +195,19 @@ Platform Utilizing Electromagnetic Fields","[74, 155, 1028, 268]",unknown_struct
Eunpyo Choi received his B.S. ","[82, 100, 1032, 449]",backmatter_body,0.6,"[""default body_paragraph for text label""]",body_paragraph,0.6,post_reference_backmatter_zone,unknown_like,none,True,True
35,0,header,Journal Pre-proof,"[505, 1, 830, 40]",frontmatter_noise,0.98,"[""journal pre-proof running header suppressed: p35 y=1/1584""]",frontmatter_noise,0.98,preproof_cover_zone,unknown_like,preproof_marker,False,False
35,1,paragraph_title,Table and Figure Captions,"[76, 102, 406, 135]",backmatter_heading,0.6,"[""unnumbered paragraph_title, inferred level subsection_heading: Table and Figure Captions""]",subsection_heading,0.6,post_reference_backmatter_zone,table_caption_like,none,True,True
35,1,paragraph_title,Table and Figure Captions,"[76, 102, 406, 135]",subsection_heading,0.6,"[""unnumbered paragraph_title, inferred level subsection_heading: Table and Figure Captions""]",subsection_heading,0.6,post_reference_backmatter_zone,table_caption_like,none,True,True
35,2,figure_title,Fig. 1. Principle idea of magnetoresponsive stem cell spheroid (MR-SCS)-based cartilage recovery platform utilizing electromagnetic fields. (a) Schematic illustration of the fabrication process of MR-,"[74, 177, 1028, 373]",figure_caption_candidate,0.92,"[""figure_title label: Fig. 1. Principle idea of magnetoresponsive stem cell sphero""]",figure_caption,0.92,post_reference_backmatter_zone,legend_like,figure_number,False,False
35,3,figure_title,Fig. 2. Characterization and chondrogenic differentiation of MR-SCS. (a) Microscopy images and average size of different concentrations of MNP-labeled (0-0.5 mg/ml) MR-SCS. Scale bar = 200 μm. (b) Via,"[74, 413, 1030, 942]",figure_caption_candidate,0.92,"[""figure_title label: Fig. 2. Characterization and chondrogenic differentiation of""]",figure_caption,0.92,post_reference_backmatter_zone,legend_like,figure_number,False,False
35,4,figure_title,Fig. 3. Magnetic actuation characterization of MR-SCS. (a) Experimental setup of the EMA system. (b) Velocity of the MR-SCS along the x- and z-axes in different concentrations of glycerol solutions (n,"[75, 979, 1032, 1422]",figure_caption_candidate,0.92,"[""figure_title label: Fig. 3. Magnetic actuation characterization of MR-SCS. (a) E""]",figure_caption,0.92,post_reference_backmatter_zone,legend_like,figure_number,False,False
36,0,header,Journal Pre-proof,"[503, 1, 832, 41]",frontmatter_noise,0.98,"[""journal pre-proof running header suppressed: p36 y=1/1584""]",frontmatter_noise,0.98,preproof_cover_zone,unknown_like,preproof_marker,False,False
36,1,figure_title,Fig. 4. Effect of LF-EMF stimulation on MR-SCS. (a) Experimental setup of the LF-EMF stimulation system and schematic illustration of the porcine osteochondral defect model. (b) Collagen type II immun,"[74, 99, 1032, 421]",figure_caption_candidate,0.92,"[""figure_title label: Fig. 4. Effect of LF-EMF stimulation on MR-SCS. (a) Experime""]",figure_caption,0.92,post_reference_backmatter_zone,legend_like,figure_number,False,False
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37,4,figure_title,Fig. 1. Principle idea of magnetoresponsive stem cell spheroid (MR-SCS)-based cartilage recovery platform utilizing electromagnetic fields. (a) Schematic illustration of the fabrication process of MR-,"[74, 478, 1032, 679]",figure_caption_candidate,0.92,"[""figure_title label: Fig. 1. Principle idea of magnetoresponsive stem cell sphero""]",figure_caption,0.92,post_reference_backmatter_zone,legend_like,figure_number,False,False
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38,3,figure_title,0 mg/mL,"[110, 210, 165, 225]",figure_caption_candidate,0.85,"[""figure_title label: 0 mg/mL""]",figure_caption,0.85,post_reference_backmatter_zone,reference_like,reference_numeric_dot,False,False
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@ -219,58 +219,58 @@ Eunpyo Choi received his B.S. ","[82, 100, 1032, 449]",backmatter_body,0.6,"[""d
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41,13,figure_title,Fig. 4. Effect of LF-EMF stimulation on MR-SCS. (a) Experimental setup of the LF-EMF stimulation system and schematic illustration of the porcine osteochondral defect model. (b) Collagen type II immun,"[74, 1169, 1032, 1491]",figure_caption_candidate,0.92,"[""figure_title label: Fig. 4. Effect of LF-EMF stimulation on MR-SCS. (a) Experime""]",figure_caption,0.92,post_reference_backmatter_zone,legend_like,figure_number,False,False

1 page block_id raw_label content_preview bbox role role_confidence evidence seed_role seed_confidence zone style_family marker_type render_default index_default
2 2 0 header Journal Pre-proof [502, 1, 832, 40] frontmatter_noise 0.98 ["journal pre-proof running header suppressed: p2 y=1/1584"] ["journal pre-proof running header suppressed: p2 y=1/1584", "page_1_preproof_cover_dropped_upstream"] frontmatter_noise 0.98 preproof_cover_zone unknown_like preproof_marker False False
3 2 1 text TITLE [76, 101, 146, 128] unknown_structural 0.3 ["short text, uncertain role"] unknown_structural 0.3 frontmatter_side_zone frontmatter_main_zone support_like short_fragment False True
4 2 2 doc_title Magnetoresponsive Stem Cell Spheroid-based Cartilage Recovery Platform Utilizing Electromagnetic Fields [74, 155, 1028, 268] unknown_structural paper_title 0.2 0.8 ["unrecognized label 'doc_title'"] ["page-1 zone title_zone: Magnetoresponsive Stem Cell Spheroid-based Cartilage Recover"] unknown_structural paper_title 0.2 0.8 body_zone frontmatter_main_zone body_like support_like none False True True
5 2 3 text Ami Yoo<sup>a</sup>, †, Gwangjun Go<sup>a</sup>, <sup>b</sup>, †, Kim Tien Nguyen<sup>a</sup>, <sup>b</sup>, Kyungmin Lee<sup>a</sup>, <sup>b</sup>, Hyun-Ki Min<sup>a</sup>, Byungjeon Kang<sup>a</sup> [74, 355, 1026, 445] frontmatter_support authors 0.6 0.8 ["default body_paragraph for text label"] ["page-1 zone author_zone: Ami Yoo<sup>a</sup>, \u2020, Gwangjun Go<sup>a</sup>, <sup>b</sup"] body_paragraph authors 0.6 0.8 body_zone frontmatter_main_zone body_like support_like none True True
6 2 4 text a. Korea Institute of Medical Microrobotics, 43-26 Cheomdangwagi-ro, Buk-gu, Gwangju, 61011, Korea [82, 535, 1028, 621] frontmatter_support unknown_structural 0.6 0.85 ["default body_paragraph for text label"] ["page-1 title fallback after pre-proof: y=535/1584"] body_paragraph paper_title 0.6 0.85 body_zone frontmatter_main_zone body_like support_like none True False True
7 2 5 text b. School of Mechanical Engineering, Chonnam National University, 77 Yongbong-ro, Buk-gu, Gwangju, 61186, Korea [81, 645, 1028, 732] frontmatter_support affiliation 0.6 0.8 ["default body_paragraph for text label"] ["page-1 zone affiliation_zone: b. School of Mechanical Engineering, Chonnam National Univer"] body_paragraph affiliation 0.6 0.8 body_zone frontmatter_main_zone body_like support_like none True True
8 2 6 text These authors contributed equally to this work. [75, 811, 548, 843] frontmatter_noise frontmatter_support 0.6 0.78 ["default body_paragraph for text label", "frontmatter_side_zone excluded from body flow"] ["first-surviving-page support text: These authors contributed equally to this work."] frontmatter_noise frontmatter_support 0.6 0.78 frontmatter_side_zone frontmatter_main_zone support_like none False True False True
9 2 7 text *Corresponding author: judyvet@jnu.ac.kr (J.H.), jop@jnu.ac.kr (J.-O.P.), and eunpyochoi@jnu.ac.kr (E.C.) [74, 864, 1032, 953] frontmatter_noise frontmatter_support 0.6 0.78 ["default body_paragraph for text label", "frontmatter_side_zone excluded from body flow"] ["first-surviving-page support text: *Corresponding author: judyvet@jnu.ac.kr (J.H.), jop@jnu.ac."] frontmatter_noise frontmatter_support 0.6 0.78 frontmatter_side_zone frontmatter_main_zone support_like none False True False True
10 2 8 paragraph_title Highlights [75, 1115, 188, 1143] structured_insert 0.7 ["structured insert label: highlights"] structured_insert_candidate 0.7 body_zone heading_like short_fragment False False
11 2 9 text - We propose the use of a magnetoresponsive stem cell spheroid (MR-SCS)-based cartilage recovery platform that allows for precise targeting using an electromagnetic actuation system to provide magneti [109, 1144, 1014, 1253] structured_insert 0.6 ["default body_paragraph for text label"] body_paragraph 0.6 body_zone body_like none False False
12 2 10 text - Locomotion of MR-SCS that was mediated by the electromagnetic actuationsystem was successfully demonstrated in 3D phantom and ex vivomodels. [111, 1278, 966, 1332] structured_insert 0.6 ["default body_paragraph for text label"] body_paragraph 0.6 body_zone body_like none False False
13 2 11 text - Low-frequency electromagnetic field stimulation of MR-SCS resulted in increased expression levels of cartilage specific markers, collagen type II, SOX9, and Aggrecan. [110, 1359, 1016, 1414] structured_insert 0.6 ["default body_paragraph for text label"] body_paragraph 0.6 body_zone body_like none False False
107 24 3 text We developed a magnetoresponsive stem cell spheroid-based cartilage recovery platform that can precisely deliver stem cells using an EMA system and can stimulate these cells by LF-EMF to promote chond [75, 1050, 1033, 1411] body_paragraph 0.6 ["default body_paragraph for text label"] body_paragraph 0.6 body_zone body_like none True True
108 25 0 header Journal Pre-proof [504, 2, 830, 39] frontmatter_noise 0.98 ["journal pre-proof running header suppressed: p25 y=2/1584"] frontmatter_noise 0.98 preproof_cover_zone unknown_like preproof_marker False False
109 25 1 text fields can provide effective potential therapeutic agents to promote the regeneration of damaged tissue within articular cartilage. [75, 100, 1031, 186] body_paragraph 0.6 ["default body_paragraph for text label", "tail_nonref_hold_zone excluded from body flow", "tail_nonref_hold_zone excluded from body flow"] ["default body_paragraph for text label"] backmatter_body body_paragraph 0.6 tail_nonref_hold_zone unknown_like none True True
110 25 2 paragraph_title Conflict of Interest [76, 241, 311, 270] backmatter_heading 0.8 ["backmatter heading on page 25: Conflict of Interest"] backmatter_heading_candidate 0.8 tail_nonref_hold_zone support_like none True True
111 25 3 text All other authors declare that they have no competing interests. [75, 359, 690, 387] backmatter_body 0.6 ["default body_paragraph for text label", "tail_nonref_hold_zone excluded from body flow"] ["default body_paragraph for text label"] backmatter_body body_paragraph 0.6 tail_nonref_hold_zone unknown_like none True True
112 25 4 paragraph_title Acknowledgments [76, 444, 304, 474] backmatter_heading 0.8 ["backmatter heading on page 25: Acknowledgments"] backmatter_heading_candidate 0.8 tail_nonref_hold_zone heading_like short_fragment True True
113 25 5 text A. Yoo and G. Go contributed equally to this work. We thank Medical Microrobot Center of Chonnam National University for supporting the equipment. This research was supported by a grant of the Korea H [74, 518, 1032, 881] backmatter_body 0.6 ["default body_paragraph for text label", "tail_nonref_hold_zone excluded from body flow"] ["default body_paragraph for text label"] backmatter_body body_paragraph 0.6 tail_nonref_hold_zone unknown_like none True True
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115 26 1 paragraph_title References [75, 102, 216, 133] reference_heading 0.9 ["references heading: References"] reference_heading 0.9 reference_zone heading_like short_fragment True True
116 26 2 reference_content [1] C. Cooper, M.K. Javaid, N. Arden, Epidemiology of osteoarthritis, Atlas of osteoarthritis, Springer 2014, pp. 21-36. [78, 166, 1027, 249] reference_item 0.85 ["reference content label: [1] C. Cooper, M.K. Javaid, N. Arden, Epidemiology of osteoa"] reference_item 0.85 reference_zone reference_like reference_numeric_bracket True True
117 26 3 reference_content [2] P. Creamer, M. Hochberg, Why does osteoarthritis of the knee hurt--sometimes?, Rheumatology, 36(1997) 726-8. [77, 275, 1027, 360] reference_item 0.85 ["reference content label: [2] P. Creamer, M. Hochberg, Why does osteoarthritis of the "] reference_item 0.85 reference_zone reference_like reference_numeric_bracket True True
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179 32 1 reference_content [58] R.G. LeBaron, K.A. Athanasiou, Ex vivo synthesis of articular cartilage, Biomaterials, 21(2000) 2575-87. [77, 99, 1028, 183] backmatter_body 0.85 ["reference content label: [58] R.G. LeBaron, K.A. Athanasiou, Ex vivo synthesis of art"] reference_item 0.85 post_reference_backmatter_zone reference_like reference_numeric_bracket True True
180 32 2 paragraph_title Biographies [81, 350, 234, 380] backmatter_heading sub_subsection_heading 0.6 ["unnumbered paragraph_title, inferred level sub_subsection_heading: Biographies"] sub_subsection_heading 0.6 post_reference_backmatter_zone heading_like short_fragment True True
181 32 3 text Amr Yoo received her B.S. (2010) degrees from the Dep. of Applied Bioscience and Biotechnology at Chonnam National University, Korea and M.S. (2013) degrees from Division of Agriculture, Food, and Nat [75, 382, 1025, 511] backmatter_body 0.6 ["default body_paragraph for text label"] body_paragraph 0.6 post_reference_backmatter_zone unknown_like none True True
182 32 4 reference_content Gwangjun Go received his B.S. (2013) and M.S. (2015) degree from the School of Mechanical Engineering at Chonnam National University, Korea. Currently, he is a Ph.D. candidate in the School of Mechani [75, 513, 1018, 619] backmatter_body 0.85 ["reference content label: Gwangjun Go received his B.S. (2013) and M.S. (2015) degree "] reference_item 0.85 post_reference_backmatter_zone unknown_like none True True
183 32 5 reference_content Kim Tien Nguyen received a B.S. degree in Dept. of Mechanical Engineering from the Hochiminh University of Technology and Education, Hochiminh, Vietnam, in 2012 and the M.S. degree in School of Mechan [75, 620, 1021, 777] backmatter_body 0.85 ["reference content label: Kim Tien Nguyen received a B.S. degree in Dept. of Mechanica"] reference_item 0.85 post_reference_backmatter_zone unknown_like none True True
184 32 6 reference_content Kyungmin Lee received her B.S. (2013) and M.S. (2018) degrees from the Dept. of Mechatronics Engineering at Korea Ploytechnic University and Mechanical Engineering of Chonnam National University, Kore [75, 779, 1021, 937] backmatter_body 0.85 ["reference content label: Kyungmin Lee received her B.S. (2013) and M.S. (2018) degree"] reference_item 0.85 post_reference_backmatter_zone unknown_like none True True
195 35 1 paragraph_title Table and Figure Captions [76, 102, 406, 135] backmatter_heading subsection_heading 0.6 ["unnumbered paragraph_title, inferred level subsection_heading: Table and Figure Captions"] subsection_heading 0.6 post_reference_backmatter_zone table_caption_like none True True
196 35 2 figure_title Fig. 1. Principle idea of magnetoresponsive stem cell spheroid (MR-SCS)-based cartilage recovery platform utilizing electromagnetic fields. (a) Schematic illustration of the fabrication process of MR- [74, 177, 1028, 373] figure_caption_candidate 0.92 ["figure_title label: Fig. 1. Principle idea of magnetoresponsive stem cell sphero"] figure_caption 0.92 post_reference_backmatter_zone legend_like figure_number False False
197 35 3 figure_title Fig. 2. Characterization and chondrogenic differentiation of MR-SCS. (a) Microscopy images and average size of different concentrations of MNP-labeled (0-0.5 mg/ml) MR-SCS. Scale bar = 200 μm. (b) Via [74, 413, 1030, 942] figure_caption_candidate 0.92 ["figure_title label: Fig. 2. Characterization and chondrogenic differentiation of"] figure_caption 0.92 post_reference_backmatter_zone legend_like figure_number False False
198 35 4 figure_title Fig. 3. Magnetic actuation characterization of MR-SCS. (a) Experimental setup of the EMA system. (b) Velocity of the MR-SCS along the x- and z-axes in different concentrations of glycerol solutions (n [75, 979, 1032, 1422] figure_caption_candidate 0.92 ["figure_title label: Fig. 3. Magnetic actuation characterization of MR-SCS. (a) E"] figure_caption 0.92 post_reference_backmatter_zone legend_like figure_number False False
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200 36 1 figure_title Fig. 4. Effect of LF-EMF stimulation on MR-SCS. (a) Experimental setup of the LF-EMF stimulation system and schematic illustration of the porcine osteochondral defect model. (b) Collagen type II immun [74, 99, 1032, 421] figure_caption_candidate 0.92 ["figure_title label: Fig. 4. Effect of LF-EMF stimulation on MR-SCS. (a) Experime"] figure_caption 0.92 post_reference_backmatter_zone legend_like figure_number False False
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273 41 13 figure_title Fig. 4. Effect of LF-EMF stimulation on MR-SCS. (a) Experimental setup of the LF-EMF stimulation system and schematic illustration of the porcine osteochondral defect model. (b) Collagen type II immun [74, 1169, 1032, 1491] figure_caption_candidate 0.92 ["figure_title label: Fig. 4. Effect of LF-EMF stimulation on MR-SCS. (a) Experime"] figure_caption 0.92 post_reference_backmatter_zone legend_like figure_number False False
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275
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tests/fixtures/ocr_real_papers/GU9R8EPE/block_trace.csv vendored Normal file
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1,4,text,"Hongji Zhang $ ^{1,2,3} $, Zheyuan Zhou $ ^{1,2,3} $, Fengjie Zhang $ ^{1,2,3} $ and Chao Wan $ ^{1,2,3,*} $","[66, 332, 801, 362]",authors,0.8,"[""page-1 zone author_zone: Hongji Zhang $ ^{1,2,3} $, Zheyuan Zhou $ ^{1,2,3} $, Feng""]",authors,0.8,frontmatter_main_zone,support_like,none,True,True
1,5,text,"Key Laboratory of Regenerative Medicine, Ministry of Education, School of Biomedical Sciences, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong SAR, China; zhan1754@link.cuhk.edu.hk","[327, 403, 1124, 470]",affiliation,0.8,"[""page-1 zone affiliation_zone: Key Laboratory of Regenerative Medicine, Ministry of Educati""]",affiliation,0.8,frontmatter_main_zone,support_like,none,True,True
1,6,text," $ ^{2} $ Center for Neuromusculoskeletal Restorative Medicine, Hong Kong Science Park, Hong Kong SAR, China
$ ^{3} $ Key Laboratory of Regenerative Medicine (Shenzhen Base), Ministry of Education, S","[326, 470, 1114, 557]",affiliation,0.8,"[""page-1 zone affiliation_zone: $ ^{2} $ Center for Neuromusculoskeletal Restorative Medicin""]",affiliation,0.8,frontmatter_main_zone,support_like,affiliation_marker,True,True
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1,8,text,"Received: 13 May 2024
Revised: 9 June 2024
Accepted: 21 June 2024
Published: 28 June 2024","[67, 1199, 221, 1292]",frontmatter_noise,0.8,"[""page-1 zone journal_furniture_zone: Received: 13 May 2024 \nRevised: 9 June 2024 \nAccepted: 21 ""]",frontmatter_noise,0.8,body_zone,body_like,none,False,False
1,9,text,"Academic Editor: Filippo Rossi
Correspondence: cwan@cuhk.edu.hk","[68, 1161, 268, 1182]",frontmatter_noise,0.8,"[""page-1 zone journal_furniture_zone: Academic Editor: Filippo Rossi\nCorrespondence: cwan@cuhk.edu""]",frontmatter_noise,0.8,body_zone,body_like,none,False,False
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1,11,text,"Citation: Zhang, H.; Zhou, Z.; Zhang, F.; Wan, C. Hydrogel-Based 3D Bioprinting Technology for Articular Cartilage Regenerative Engineering. Gels 2024, 10, 430. https://doi.org/10.3390/gels10070430","[66, 1006, 306, 1147]",frontmatter_noise,0.8,"[""page-1 zone journal_furniture_zone: Citation: Zhang, H.; Zhou, Z.; Zhang, F.; Wan, C. Hydrogel-B""]",frontmatter_noise,0.8,frontmatter_main_zone,support_like,none,False,False
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1,13,abstract,"Abstract: Articular cartilage is an avascular tissue with very limited capacity of self-regeneration. Trauma or injury-related defects, inflammation, or aging in articular cartilage can induce progres","[327, 604, 1123, 995]",abstract_body,0.85,"[""abstract label from Paddle OCR""]",abstract_body,0.85,frontmatter_main_zone,support_like,none,True,True
1,14,text,"Copyright: © 2024 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) licen","[66, 1363, 309, 1551]",frontmatter_noise,0.8,"[""page-1 zone journal_furniture_zone: Copyright: \u00a9 2024 by the authors.\nLicensee MDPI, Basel, Swit""]",frontmatter_noise,0.8,body_zone,support_like,none,False,False
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1,17,text,"Osteoarthritis (OA) is one of the most common degenerative joint diseases with a high disability rate, whose incidence increases with an aging population and increasing obesity rates. OA is becoming i","[324, 1148, 1124, 1298]",body_paragraph,0.6,"[""default body_paragraph for text label""]",body_paragraph,0.6,body_zone,body_like,none,True,True
1,18,text,"Natural cartilage tissue is avascular and nerve-free, which limits its ability to repair itself. It has been reported that articular cartilage defects over 2 cm² in size in the human knee are hardly s","[325, 1299, 1127, 1552]",body_paragraph,0.6,"[""default body_paragraph for text label""]",body_paragraph,0.6,body_zone,body_like,none,True,True
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2,2,text,"in cartilage repair. Using the traditional engineering approaches, the bio-mimic structure of articular cartilage is difficult to be rebuilt due to the fact that natural cartilage tissue has a complic","[325, 176, 1125, 402]",body_paragraph,0.6,"[""default body_paragraph for text label""]",body_paragraph,0.6,body_zone,body_like,none,True,True
2,3,text,"Three-dimensional bioprinting relies on two main factors, including (1) the design of bioink as precursors (biomaterials, cells, growth factors and drugs); (2) the use of the 3D bioprinting method to ","[324, 402, 1126, 731]",body_paragraph,0.6,"[""default body_paragraph for text label""]",body_paragraph,0.6,body_zone,body_like,none,True,True
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2,5,figure_title,"Figure 1. Schematic diagram of 3D bioprinted hydrogel for articular cartilage tissue engineering. Chondrocytes, mesenchymal stem cells (MSCs) or chondroprogenitor cells are isolated, cultured and expa","[326, 1268, 1125, 1448]",figure_caption,0.92,"[""figure_title label: Figure 1. Schematic diagram of 3D bioprinted hydrogel for ar""]",figure_caption,0.92,display_zone,legend_like,figure_number,True,True
2,6,text,"In this review, we mainly summarized the current progress of different types of hydrogel biomaterials, including natural and synthetic hydrogels used for articular cartilage","[326, 1466, 1124, 1519]",body_paragraph,0.6,"[""default body_paragraph for text label""]",body_paragraph,0.6,body_zone,body_like,none,True,True
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3,2,text,repair or regeneration. The challenges and improvement directions of these hydrogels in cartilage tissue engineering applications are also stated.,"[326, 176, 1122, 228]",body_paragraph,0.6,"[""default body_paragraph for text label""]",body_paragraph,0.6,body_zone,body_like,none,True,True
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3,4,text,"Cartilage is a resilient, avascular and aneural connective tissue, consisting of chondrocytes in an ECM that is rich in collagen type II and proteoglycan. Some studies indicate that the average thickn","[323, 276, 1126, 783]",body_paragraph,0.6,"[""default body_paragraph for text label""]",body_paragraph,0.6,body_zone,body_like,none,True,True
3,5,image,,"[331, 817, 1006, 1076]",media_asset,0.85,"[""media label: image""]",media_asset,0.85,body_zone,unknown_like,empty,True,True
3,6,figure_title,"Figure 2. Schematic representation of articular cartilage structures in the knee joint. The structure of articular cartilage is organized into four distinct zones: the superficial, transitional, radia","[325, 1108, 1124, 1209]",figure_caption,0.92,"[""figure_title label: Figure 2. Schematic representation of articular cartilage st""]",figure_caption,0.92,display_zone,legend_like,figure_number,True,True
3,7,text,"In the fetal period, infancy, or childhood, articular cartilage has its own regenerative activity. However, articular cartilage cannot be regenerated in the adults because mature chondrocytes do not p","[324, 1228, 1124, 1502]",body_paragraph,0.6,"[""default body_paragraph for text label""]",body_paragraph,0.6,body_zone,body_like,none,True,True
3,8,text,"It is well known that collagens in the ECM, such as collagen type II, IX, X, and XI determine the tensile strength of articular cartilage. The amount of water in articular","[326, 1503, 1125, 1553]",body_paragraph,0.6,"[""default body_paragraph for text label""]",body_paragraph,0.6,body_zone,body_like,none,True,True
4,0,header,"Gels 2024, 10, 430","[67, 99, 195, 119]",noise,0.9,"[""header label""]",noise,0.9,body_zone,body_like,short_fragment,False,False
4,1,header,4 of 35,"[1069, 103, 1121, 123]",noise,0.9,"[""header label""]",noise,0.9,,reference_like,reference_numeric_dot,False,False
4,2,text,"cartilage and the number of chemical bonds present in collagen molecules decrease with age. As a result, the articular cartilage of the elderly is prone to weakened elasticity and lacks effective resi","[324, 176, 1123, 301]",body_paragraph,0.6,"[""default body_paragraph for text label""]",body_paragraph,0.6,body_zone,body_like,none,True,True
4,3,text,"At present, the conservative treatments for articular cartilage injury mainly include anti-inflammatory, analgesic, and drug therapy, in addition to weight-bearing and physical therapy. However, many ","[324, 301, 1125, 728]",body_paragraph,0.6,"[""default body_paragraph for text label""]",body_paragraph,0.6,body_zone,body_like,none,True,True
4,4,text,"Therefore, in view of the problems mentioned above, it is necessary to develop and optimize better methods to repair articular cartilage lesions. More importantly, the re-generated cartilage tissue ha","[325, 728, 1125, 831]",body_paragraph,0.6,"[""default body_paragraph for text label""]",body_paragraph,0.6,body_zone,body_like,none,True,True
4,5,paragraph_title,3. Tissue Engineering for Treatment of Cartilage Damages,"[327, 848, 864, 873]",section_heading,0.85,"[""paragraph_title label with numbering: 3. Tissue Engineering for Treatment of Cartilage Damages""]",section_heading,0.85,body_zone,reference_like,reference_numeric_dot,True,True
4,6,text,"Cartilage tissue engineering is an interdisciplinary field that applies the principles of engineering and life sciences to develop biological substitutes that restore, maintain, or improve the structu","[324, 878, 1125, 1487]",body_paragraph,0.6,"[""default body_paragraph for text label""]",body_paragraph,0.6,body_zone,body_like,none,True,True
5,0,header,"Gels 2024, 10, 430","[67, 99, 195, 119]",noise,0.9,"[""header label""]",noise,0.9,body_zone,body_like,short_fragment,False,False
5,1,header,5 of 35,"[1069, 103, 1121, 123]",noise,0.9,"[""header label""]",noise,0.9,,reference_like,reference_numeric_dot,False,False
5,2,paragraph_title,4. Three-Dimensional Bioprinting Materials for Articular Cartilage Repair or Regeneration,"[326, 176, 1012, 223]",section_heading,0.85,"[""paragraph_title label with numbering: 4. Three-Dimensional Bioprinting Materials for Articular Car""]",section_heading,0.85,body_zone,reference_like,reference_numeric_dot,True,True
5,3,text,"Up to now, 3D printing technology has been considered to be one of the most promising technologies for cartilage tissue engineering, replacing damaged or non-functional cartilage tissue with 3D-printe","[324, 229, 1125, 580]",body_paragraph,0.6,"[""default body_paragraph for text label""]",body_paragraph,0.6,body_zone,body_like,none,True,True
5,4,text,"Currently, a variety of technologies have been developed to fulfil the requirements of 3D bioprinting. Among them, the most commonly used methods are extrusion-based bioprinting, inkjet bioprinting, l","[325, 580, 1123, 680]",body_paragraph,0.6,"[""default body_paragraph for text label""]",body_paragraph,0.6,body_zone,body_like,none,True,True
5,5,text,"(1) Extrusion-based bioprinting methods typically use a pneumatic actuator or screw device to feed material from a cartridge through a nozzle or needle for deposition. These methods, compatible with a","[325, 681, 1125, 1133]",body_paragraph,0.78,"[""default body_paragraph for text label"", ""late role resolution: non-body family 'reference_like' overrides body_paragraph"", ""style_family_authority=reference_marker"", ""context_source=block""]",body_paragraph,0.6,body_zone,reference_like,reference_numeric_parenthesis,True,True
5,6,text,"(2) Inkjet-based bioprinting is a non-contact printing method derived from desktop inkjet printing, where individual droplets create patterns on a substrate. During bioprinting, an energy drive deform","[325, 1132, 1126, 1460]",body_paragraph,0.78,"[""default body_paragraph for text label"", ""late role resolution: non-body family 'reference_like' overrides body_paragraph"", ""style_family_authority=reference_marker"", ""context_source=block""]",body_paragraph,0.6,body_zone,reference_like,reference_numeric_parenthesis,True,True
6,0,header,"Gels 2024, 10, 430","[67, 99, 195, 119]",noise,0.9,"[""header label""]",noise,0.9,body_zone,body_like,short_fragment,False,False
6,1,number,6 of 35,"[1069, 103, 1121, 123]",noise,0.9,"[""page number label""]",noise,0.9,,reference_like,reference_numeric_dot,False,False
6,2,image,,"[336, 178, 1111, 941]",media_asset,0.85,"[""media label: image""]",media_asset,0.85,body_zone,unknown_like,empty,True,True
6,3,figure_title,"Figure 3. Schematic diagram of four methods of 3D bioprinting for articular cartilage tissue engineering. (a) Inkjet printers extrude cells and bioinks in the form of droplets, driven by thermal energ","[326, 969, 1125, 1148]",figure_caption,0.92,"[""figure_title label: Figure 3. Schematic diagram of four methods of 3D bioprintin""]",figure_caption,0.92,display_zone,legend_like,figure_number,True,True
6,4,text,"(3) Laser-assisted bioprinting utilizes a laser as the energy source to deposit biomaterials onto a substrate. This technique typically comprises three components: a pulsed laser source, a ribbon coat","[325, 1167, 1126, 1467]",body_paragraph,0.78,"[""default body_paragraph for text label"", ""late role resolution: non-body family 'reference_like' overrides body_paragraph"", ""style_family_authority=reference_marker"", ""context_source=block""]",body_paragraph,0.6,body_zone,reference_like,reference_numeric_parenthesis,True,True
6,5,text,(4) Stereolithography bioprinting selectively solidifies a cell-laden bioink using photopolymerization in a layer-by-layer process controlled by a moveable stage along the z-axis. A 2D pattern is proj,"[325, 1468, 1127, 1546]",body_paragraph,0.78,"[""default body_paragraph for text label"", ""late role resolution: non-body family 'reference_like' overrides body_paragraph"", ""style_family_authority=reference_marker"", ""context_source=block""]",body_paragraph,0.6,body_zone,reference_like,reference_numeric_parenthesis,True,True
7,0,header,"Gels 2024, 10, 430","[68, 99, 195, 119]",noise,0.9,"[""header label""]",noise,0.9,body_zone,body_like,short_fragment,False,False
7,1,header,7 of 35,"[1069, 103, 1121, 123]",noise,0.9,"[""header label""]",noise,0.9,,reference_like,reference_numeric_dot,False,False
7,2,text,"3D structures without a printhead moving in the x-y directions. This results in a faster bioprinting process compared to other nozzle-based bioprinters [57]. Unlike traditional extrusion printing, ste","[326, 176, 1124, 402]",body_paragraph,0.6,"[""default body_paragraph for text label"", ""promoted in tail spread from body_paragraph""]",body_paragraph,0.6,body_zone,body_like,none,True,True
7,3,text,"Bioink is one of the major elements for 3D bioprinting. There are many biomaterials that can be used as bioinks. Among these biomaterials, hydrogels play the most important role in 3D bioprinting and ","[325, 402, 1124, 731]",body_paragraph,0.6,"[""default body_paragraph for text label"", ""promoted in tail spread from body_paragraph""]",body_paragraph,0.6,body_zone,body_like,none,True,True
7,4,paragraph_title,4.1. Natural Hydrogels,"[328, 748, 525, 773]",subsection_heading,0.85,"[""paragraph_title label with numbering: 4.1. Natural Hydrogels""]",subsection_heading,0.85,body_zone,heading_like,heading_numbered,True,True
7,5,text,"The natural hydrogel is mainly based on proteins and polysaccharides of the extracellular matrix (ECM). The protein-based hydrogels include gelatin, fibrin, elastin, and silk fibroin. The polysacchari","[325, 779, 1126, 1059]",body_paragraph,0.6,"[""default body_paragraph for text label"", ""promoted in tail spread from body_paragraph""]",body_paragraph,0.6,body_zone,body_like,none,True,True
8,0,header,"Gels 2024, 10, 430","[67, 99, 198, 119]",noise,0.9,"[""header label""]",noise,0.9,body_zone,body_like,short_fragment,False,False
8,1,number,8 of 35,"[1560, 104, 1617, 123]",noise,0.9,"[""page number label""]",noise,0.9,,reference_like,reference_numeric_dot,False,False
8,2,figure_title,Table 1. Overview of some used natural hydrogels for cartilage tissue engineering.,"[325, 175, 992, 199]",table_caption,0.9,"[""table prefix matched: Table 1. Overview of some used natural hydrogels for cartila""]",table_caption,0.9,display_zone,table_caption_like,table_number,True,True
8,3,table,"<table><tr><td>Name</td><td>Chemical</td><td>Advantages</td><td>Limitations</td><td>References</td></tr><tr><td>Alginate</td><td><img src=""imgs/img_in_image_box_360_248_595_348.jpg"" alt=""Image"""" /></t","[69, 210, 1615, 735]",media_asset,0.85,"[""media label: table""]",media_asset,0.85,body_zone,body_like,none,True,True
9,0,header,"Gels 2024, 10, 430","[67, 99, 195, 119]",noise,0.9,"[""header label""]",noise,0.9,body_zone,body_like,short_fragment,False,False
9,1,number,9 of 35,"[1069, 103, 1121, 123]",noise,0.9,"[""page number label""]",noise,0.9,,reference_like,reference_numeric_dot,False,False
9,2,paragraph_title,4.1.1. Alginate,"[327, 176, 465, 200]",subsection_heading,0.85,"[""paragraph_title label with numbering: 4.1.1. Alginate""]",subsection_heading,0.85,body_zone,heading_like,heading_numbered,True,True
9,3,text,"Alginate is a natural polysaccharide extracted from brown algae, which is highly hydrophilic and biocompatible. It is characterized by being non-cytotoxic, cost-effective, and eliciting a low immune r","[324, 207, 1124, 483]",body_paragraph,0.6,"[""default body_paragraph for text label"", ""promoted in tail spread from body_paragraph""]",body_paragraph,0.6,body_zone,body_like,none,True,True
9,4,text,"However, alginate hydrogels are easily degraded over time in vivo due to the loss of calcium ions and thus are also considered to be lacking the biological stability of scaffolds. In the past decade, ","[325, 483, 1125, 910]",body_paragraph,0.6,"[""default body_paragraph for text label"", ""promoted in tail spread from body_paragraph""]",body_paragraph,0.6,body_zone,body_like,none,True,True
9,5,text,"Kosik-Kozioł et al. formulated an alginate/short submicron polylactide ink, which was able to increase Young's modulus of bioprinted scaffold and neocartilage ECM deposition [82]. Kilian et al. fabric","[325, 910, 1125, 1387]",body_paragraph,0.6,"[""default body_paragraph for text label"", ""promoted in tail spread from body_paragraph""]",body_paragraph,0.6,body_zone,body_like,none,True,True
9,6,text,"Yu X et al. formulated a double-layer hydrogel scaffold by using agarose as a base material, in which a sodium alginate (SA)/agarose layer was used for the repair of artificially produced subchondral ","[325, 1388, 1125, 1539]",body_paragraph,0.78,"[""default body_paragraph for text label"", ""late role resolution: non-body family 'reference_like' overrides body_paragraph"", ""style_family_authority=reference_marker"", ""context_source=block""]",body_paragraph,0.6,body_zone,reference_like,citation_line,True,True
10,0,header,"Gels 2024, 10, 430","[67, 99, 195, 119]",noise,0.9,"[""header label""]",noise,0.9,body_zone,body_like,short_fragment,False,False
10,1,header,10 of 35,"[1062, 103, 1121, 123]",noise,0.9,"[""header label""]",noise,0.9,,reference_like,reference_numeric_dot,False,False
10,2,text,a preliminary calcified layer. This clearly showed that in situ fabrication of an anisotropic double-layer hydrogel as a bioscaffold could promote the functional repair of articular cartilage and subc,"[325, 176, 1123, 275]",body_paragraph,0.6,"[""default body_paragraph for text label"", ""promoted in tail spread from body_paragraph""]",body_paragraph,0.6,body_zone,body_like,none,True,True
10,3,text,"Despite these advancements, there are several drawbacks associated with using alginate for cartilage tissue engineering. For example, due to the lack of adhesive ligands and the existence of negative ","[324, 278, 1125, 603]",body_paragraph,0.6,"[""default body_paragraph for text label"", ""promoted in tail spread from body_paragraph""]",body_paragraph,0.6,body_zone,body_like,none,True,True
10,4,paragraph_title,4.1.2. HA,"[328, 622, 420, 645]",subsection_heading,0.85,"[""paragraph_title label with numbering: 4.1.2. HA""]",subsection_heading,0.85,body_zone,heading_like,heading_numbered,True,True
10,5,text,"HA is a natural glycosaminoglycan characterized by repeating disaccharide units of glucuronic acid and N-acetylglucosamine [93]. As one of the major components in the ECM, HA significantly enhances ch","[324, 653, 1125, 854]",body_paragraph,0.6,"[""default body_paragraph for text label"", ""promoted in tail spread from body_paragraph""]",body_paragraph,0.6,body_zone,body_like,none,True,True
10,6,text,"In addition, aggrecan and other proteoglycans, which are the matrix components, can also act as receptors for HA and promotes chondrogenesis [96]. Due to its unique relationship with chondrocytes, HA ","[325, 855, 1124, 955]",body_paragraph,0.6,"[""default body_paragraph for text label"", ""promoted in tail spread from body_paragraph""]",body_paragraph,0.6,body_zone,body_like,none,True,True
10,7,text,"Current research efforts primarily focus on the application of HA-based injectable hydrogels and the use as 3D printing bioinks. Injectable HA hydrogels, including aldehyde HA-based, thiolated HA-base","[324, 954, 1127, 1535]",body_paragraph,0.6,"[""default body_paragraph for text label"", ""promoted in tail spread from body_paragraph""]",body_paragraph,0.6,body_zone,body_like,none,True,True
11,0,header,"Gels 2024, 10, 430","[67, 99, 195, 119]",noise,0.9,"[""header label""]",noise,0.9,body_zone,body_like,short_fragment,False,False
11,1,header,11 of 35,"[1061, 103, 1121, 123]",noise,0.9,"[""header label""]",noise,0.9,,reference_like,reference_numeric_dot,False,False
11,2,text,"ELP (ELP-HYD) and aldehyde-modified HA (HA-ALD). The hydrogels were fabricated with variable HA concentrations (1.5%, 3%, or 5%), and chondrocytes were encapsulated. The results showed that increasing","[324, 176, 1124, 577]",body_paragraph,0.6,"[""default body_paragraph for text label"", ""promoted in tail spread from body_paragraph""]",body_paragraph,0.6,body_zone,body_like,none,True,True
11,3,text,"HA is emerging as a promising biomaterial applied in the cartilage tissue engineering, given its biocompatibility, biodegradation, and the favorable biomimetic environment it creates, promoting cell a","[324, 577, 1124, 756]",body_paragraph,0.6,"[""default body_paragraph for text label"", ""promoted in tail spread from body_paragraph""]",body_paragraph,0.6,body_zone,body_like,none,True,True
11,4,paragraph_title,4.1.3. Collagen,"[328, 773, 468, 798]",subsection_heading,0.85,"[""paragraph_title label with numbering: 4.1.3. Collagen""]",subsection_heading,0.85,body_zone,heading_like,heading_numbered,True,True
11,5,text,"Collagen exists mainly in the connective tissue of mammals and is an important component of the ECM. It is widely found in the bones, tendons, muscle sheaths, and in the membranes, ligaments, cartilag","[325, 804, 1124, 1057]",body_paragraph,0.6,"[""default body_paragraph for text label"", ""promoted in tail spread from body_paragraph""]",body_paragraph,0.6,body_zone,body_like,none,True,True
11,6,text,"In the field of cartilage tissue engineering, COL I and COL II are the most commonly used for cartilage regeneration. Yang et al. used COL I or agarose (AG) mixed with sodium alginate (SA) to serve as","[326, 1055, 1125, 1534]",body_paragraph,0.6,"[""default body_paragraph for text label"", ""promoted in tail spread from body_paragraph""]",body_paragraph,0.6,body_zone,body_like,none,True,True
12,0,header,"Gels 2024, 10, 430","[67, 99, 195, 119]",noise,0.9,"[""header label""]",noise,0.9,body_zone,body_like,short_fragment,False,False
12,1,header,12 of 35,"[1061, 103, 1121, 123]",noise,0.9,"[""header label""]",noise,0.9,,reference_like,reference_numeric_dot,False,False
12,2,text,"Moreover, it played pivotal roles in promoting the chondrogenic differentiation of BMSCs and regenerating the cartilage defects in vivo [113]. However, directly applying collagen as a source for 3D bi","[322, 175, 1127, 1055]",body_paragraph,0.6,"[""default body_paragraph for text label"", ""promoted in tail spread from body_paragraph""]",body_paragraph,0.6,body_zone,body_like,none,True,True
12,3,text,"In cartilage tissue engineering, collagen-based cartilage scaffolds can enhance the expression of chondrogenic marker genes and promote cartilage regeneration, which effectively reduces the risk of im","[324, 1055, 1126, 1334]",body_paragraph,0.6,"[""default body_paragraph for text label"", ""promoted in tail spread from body_paragraph""]",body_paragraph,0.6,body_zone,body_like,none,True,True
12,4,paragraph_title,4.1.4. SF,"[328, 1350, 410, 1373]",subsection_heading,0.85,"[""paragraph_title label with numbering: 4.1.4. SF""]",subsection_heading,0.85,body_zone,heading_like,heading_numbered,True,True
12,5,text,"SF is recognized as a natural biomaterial, spun by silkworms and spiders [123]. It is composed of 43% glycine, 30% alanine, and 12% serine [124]. The SF fiber consists of bundles of interlocking nanof","[325, 1381, 1125, 1534]",body_paragraph,0.6,"[""default body_paragraph for text label"", ""promoted in tail spread from body_paragraph""]",body_paragraph,0.6,body_zone,body_like,none,True,True
13,0,header,"Gels 2024, 10, 430","[67, 99, 195, 119]",noise,0.9,"[""header label""]",noise,0.9,body_zone,body_like,short_fragment,False,False
13,1,header,13 of 35,"[1062, 103, 1122, 123]",noise,0.9,"[""header label""]",noise,0.9,,reference_like,reference_numeric_dot,False,False
13,2,text,"SF, SF needs to undergo processes such as degumming, washing, and drying $ [126] $. Similar to the biomaterials mentioned above, SF also exhibits various characterizations, such as favorable mechanic","[324, 175, 1125, 351]",body_paragraph,0.6,"[""default body_paragraph for text label"", ""promoted in tail spread from body_paragraph""]",body_paragraph,0.6,body_zone,body_like,none,True,True
13,3,text,"Due to its exceptional biocompatibility, SF serves as an ideal 3D matrix and bioscaffold for enhancing cell adhesion, proliferation, and differentiation in tissue engineering. Moreover, owing to its d","[323, 351, 1128, 1536]",body_paragraph,0.6,"[""default body_paragraph for text label"", ""promoted in tail spread from body_paragraph""]",body_paragraph,0.6,body_zone,body_like,none,True,True
14,0,header,"Gels 2024, 10, 430","[67, 99, 195, 119]",noise,0.9,"[""header label""]",noise,0.9,body_zone,body_like,short_fragment,False,False
14,1,header,14 of 35,"[1062, 103, 1121, 123]",noise,0.9,"[""header label""]",noise,0.9,,reference_like,reference_numeric_dot,False,False
14,2,text,"high efficacy of SF-HPCMA in repairing rabbit articular cartilage defects, indicating its therapeutic potential for clinical applications $ [140] $.","[325, 176, 1122, 226]",body_paragraph,0.6,"[""default body_paragraph for text label"", ""promoted in tail spread from body_paragraph""]",body_paragraph,0.6,body_zone,body_like,none,True,True
14,3,text,"Despite the fact that SF possess great biocompatibility, low immunogenicity, and slow degradation, the SF materials and scaffolds formed from SF for tissue engineering sometimes show biological and me","[326, 403, 1124, 604]",body_paragraph,0.6,"[""default body_paragraph for text label"", ""promoted in tail spread from body_paragraph""]",body_paragraph,0.6,body_zone,body_like,none,True,True
14,4,text,"In addition, a decellularized extracellular matrix (dECM) was applied as a natural biomaterial similar to SF for tissue regeneration. Zhou et al. constructed dECM-SF hydrogel by a second crosslinking ","[325, 227, 1124, 401]",body_paragraph,0.6,"[""default body_paragraph for text label"", ""promoted in tail spread from body_paragraph""]",body_paragraph,0.6,body_zone,body_like,none,True,True
14,5,paragraph_title,4.2. Synthetic Hydrogels,"[329, 622, 538, 647]",subsection_heading,0.85,"[""paragraph_title label with numbering: 4.2. Synthetic Hydrogels""]",subsection_heading,0.85,body_zone,heading_like,heading_numbered,True,True
14,6,text,"Although natural hydrogels possess many advantages, such as low immunogenicity, great biocompatibility, a favorable biomimetic environment and cell growth, they also have drawbacks, including low mech","[325, 652, 1125, 1060]",body_paragraph,0.6,"[""default body_paragraph for text label"", ""promoted in tail spread from body_paragraph""]",body_paragraph,0.6,body_zone,body_like,none,True,True
15,0,header,"Gels 2024, 10, 430","[66, 99, 198, 119]",noise,0.9,"[""header label""]",noise,0.9,body_zone,body_like,short_fragment,False,False
15,1,number,15 of 35,"[1553, 103, 1617, 123]",noise,0.9,"[""page number label""]",noise,0.9,,reference_like,reference_numeric_dot,False,False
15,2,figure_title,Table 2. Overview of some used synthetic hydrogels for cartilage tissue engineering.,"[326, 175, 1007, 199]",table_caption,0.9,"[""table prefix matched: Table 2. Overview of some used synthetic hydrogels for carti""]",table_caption,0.9,display_zone,table_caption_like,table_number,True,True
15,3,table,"<table><tr><td>Name</td><td>Chemical</td><td>Advantages</td><td>Limitations</td><td>References</td></tr><tr><td>Polyethylene glycol (PEG)</td><td><img src=""imgs/img_in_image_box_431_275_634_366.jpg"" a","[68, 206, 1615, 896]",media_asset,0.85,"[""media label: table""]",media_asset,0.85,body_zone,body_like,none,True,True
16,0,header,"Gels 2024, 10, 430","[66, 99, 198, 119]",noise,0.9,"[""header label""]",noise,0.9,body_zone,body_like,short_fragment,False,False
16,1,number,16 of 35,"[1552, 103, 1617, 123]",noise,0.9,"[""page number label""]",noise,0.9,,reference_like,reference_numeric_dot,False,False
16,2,figure_title,Table 2. Cont.,"[326, 175, 447, 197]",table_caption,0.9,"[""table prefix matched: Table 2. Cont.""]",table_caption,0.9,display_zone,table_caption_like,table_number,True,True
16,3,table,"<table><tr><td>Name</td><td>Chemical</td><td>Advantages</td><td>Limitations</td><td>References</td></tr><tr><td>Hyaluronic acid methacrylate (HAMA)</td><td><img src=""imgs/img_in_image_box_443_273_627_","[71, 209, 1612, 818]",media_asset,0.85,"[""media label: table""]",media_asset,0.85,body_zone,body_like,none,True,True
17,0,header,"Gels 2024, 10, 430","[67, 99, 195, 119]",noise,0.9,"[""header label""]",noise,0.9,body_zone,body_like,short_fragment,False,False
17,1,number,17 of 35,"[1062, 103, 1121, 123]",noise,0.9,"[""page number label""]",noise,0.9,,reference_like,reference_numeric_dot,False,False
17,2,paragraph_title,4.2.1. PEG-Based Hydrogel,"[327, 175, 577, 201]",subsection_heading,0.85,"[""paragraph_title label with numbering: 4.2.1. PEG-Based Hydrogel""]",subsection_heading,0.85,body_zone,heading_like,heading_numbered,True,True
17,3,text,"As an FDA-approved synthetic polymer, polyethylene glycol (PEG) assumes a vital role in the field of biomedical applications such as tissue engineering and drug delivery, particularly in cartilage reg","[324, 207, 1125, 434]",body_paragraph,0.6,"[""default body_paragraph for text label"", ""promoted in tail spread from body_paragraph""]",body_paragraph,0.6,body_zone,body_like,none,True,True
17,4,text,"In recent years, photopolymerization, utilizing light to convert liquid PEG macromer solutions into solid hydrogels under physiological conditions, has been developed and plays a unique and primary ro","[324, 433, 1125, 911]",body_paragraph,0.6,"[""default body_paragraph for text label"", ""promoted in tail spread from body_paragraph""]",body_paragraph,0.6,body_zone,body_like,none,True,True
17,5,text,"Although photopolymerization stands out as a fast and convenient method for creating PEG hydrogels, significant apprehensions emerge concerning cell exposure to light, particularly in the ultraviolet ","[324, 911, 1124, 1287]",body_paragraph,0.6,"[""default body_paragraph for text label"", ""promoted in tail spread from body_paragraph""]",body_paragraph,0.6,body_zone,body_like,none,True,True
17,6,text,"In addition, PEG hydrogels have been extensively utilized as matrices to control drug delivery and as vehicles for cell delivery for tissue regeneration because of their optimal biodegradability, hydr","[325, 1287, 1126, 1540]",body_paragraph,0.6,"[""default body_paragraph for text label"", ""promoted in tail spread from body_paragraph""]",body_paragraph,0.6,body_zone,body_like,none,True,True
18,0,header,"Gels 2024, 10, 430","[67, 99, 195, 119]",noise,0.9,"[""header label""]",noise,0.9,body_zone,body_like,short_fragment,False,False
18,1,header,18 of 35,"[1062, 103, 1121, 123]",noise,0.9,"[""header label""]",noise,0.9,,reference_like,reference_numeric_dot,False,False
18,2,text,"ing with cysteine–arginine–glycine–aspartic acid (CRGA, a cell adhesion peptide) via the Michael Addition Reaction. Due to the improved cell adhesion capability and bioactivity, CRGA-conjugated PEG fa","[324, 176, 1125, 352]",body_paragraph,0.6,"[""default body_paragraph for text label"", ""promoted in tail spread from body_paragraph""]",body_paragraph,0.6,body_zone,body_like,none,True,True
18,3,paragraph_title,4.2.2. GelMA-Based Hydrogel,"[327, 371, 602, 396]",subsection_heading,0.85,"[""paragraph_title label with numbering: 4.2.2. GelMA-Based Hydrogel""]",subsection_heading,0.85,body_zone,heading_like,heading_numbered,True,True
18,4,text,"Gelatin, a protein found in the ECM and derived from denatured collagen, is a natural material with biocompatible and biodegradable properties. Although gelatin itself forms a gelatinous structure, it","[325, 402, 1124, 629]",body_paragraph,0.6,"[""default body_paragraph for text label"", ""promoted in tail spread from body_paragraph""]",body_paragraph,0.6,body_zone,body_like,none,True,True
18,5,text,"GelMA is produced by reacting gelatin with methacrylic anhydride (MA). Briefly, this reaction replaces numerous amino groups on gelatin's side chains with methacryloyl groups and forms a synthetic mat","[324, 631, 1126, 1535]",body_paragraph,0.6,"[""default body_paragraph for text label"", ""promoted in tail spread from body_paragraph""]",body_paragraph,0.6,body_zone,body_like,none,True,True
19,0,header,"Gels 2024, 10, 430","[67, 98, 195, 119]",noise,0.9,"[""header label""]",noise,0.9,body_zone,body_like,short_fragment,False,False
19,1,header,19 of 35,"[1062, 102, 1121, 123]",noise,0.9,"[""header label""]",noise,0.9,,reference_like,reference_numeric_dot,False,False
19,2,text,"which had a relatively lower elastic modulus and was conducive to the differentiation of BMSCs into chondrocytes. In the lower scaffold, in addition to BP and hUMSCs exos, $ \beta $-tricalcium phosph","[325, 176, 1126, 604]",body_paragraph,0.6,"[""default body_paragraph for text label"", ""promoted in tail spread from body_paragraph""]",body_paragraph,0.6,body_zone,body_like,none,True,True
19,3,text,"In conclusion, GelMA plays a unique and irreplaceable role in biomedical applications because of its advantageous biocompatibility, bioactivity, and permeability (Figure 4). Although its mechanical pr","[324, 603, 1125, 757]",body_paragraph,0.6,"[""default body_paragraph for text label"", ""promoted in tail spread from body_paragraph""]",body_paragraph,0.6,body_zone,body_like,none,True,True
19,4,image,,"[331, 781, 991, 1465]",media_asset,0.85,"[""media label: image""]",media_asset,0.85,body_zone,unknown_like,empty,True,True
19,5,figure_title,Figure 4. Schematic illustration of GelMA-based hydrogels. (A) The GelMA-glycol chitosan hydrogel exhibited excellent mechanical properties and an appropriate degradation period while maintaining,"[326, 1490, 1125, 1543]",figure_caption,0.92,"[""figure_title label: Figure 4. Schematic illustration of GelMA-based hydrogels. (""]",figure_caption,0.92,display_zone,legend_like,figure_number,True,True
20,0,header,"Gels 2024, 10, 430","[67, 99, 195, 119]",noise,0.9,"[""header label""]",noise,0.9,body_zone,body_like,short_fragment,False,False
20,1,header,20 of 35,"[1061, 103, 1121, 123]",noise,0.9,"[""header label""]",noise,0.9,,reference_like,reference_numeric_dot,False,False
20,2,text,high adhesive capability. (B) The increased expression of chondrogenic markers in encapsulated BMSCs suggested that Cytomodulin-10-modified GelMA with Kartogenin holds significant potential for enhanc,"[326, 187, 1125, 391]",body_paragraph,0.6,"[""default body_paragraph for text label"", ""promoted in tail spread from body_paragraph""]",body_paragraph,0.6,body_zone,body_like,none,True,True
20,3,paragraph_title,4.2.3. PLA- and PLA Copolymer-Based Hydrogel,"[327, 406, 772, 431]",subsection_heading,0.85,"[""paragraph_title label with numbering: 4.2.3. PLA- and PLA Copolymer-Based Hydrogel""]",subsection_heading,0.85,body_zone,heading_like,heading_numbered,True,True
20,4,text,"Polylactic acid (PLA), a semi-crystalline polymer with a gradual crystallization process, distinguishes itself in the field of biomedical applications owing to its compatibility with host tissues, hyd","[325, 437, 1125, 638]",body_paragraph,0.6,"[""default body_paragraph for text label"", ""promoted in tail spread from body_paragraph""]",body_paragraph,0.6,body_zone,body_like,none,True,True
20,5,text,"The intermolecular forces governing PLA chains involve hydrogen bonding, hydrophobic, and ionic interactions. While the inherent hydrophobicity is advantageous for trapping drug-loaded nanoparticles, ","[324, 641, 1125, 1465]",body_paragraph,0.6,"[""default body_paragraph for text label"", ""promoted in tail spread from body_paragraph""]",body_paragraph,0.6,body_zone,body_like,none,True,True
20,6,text,"In clinical applications, PLA stands out as one of the most prevalent biodegradable polymers. Despite the hydrophobic nature of these polymers and their lack of functional side groups for chemical cro","[324, 1466, 1124, 1545]",body_paragraph,0.6,"[""default body_paragraph for text label"", ""promoted in tail spread from body_paragraph""]",body_paragraph,0.6,body_zone,body_like,none,True,True
21,0,header,"Gels 2024, 10, 430","[67, 99, 195, 119]",noise,0.9,"[""header label""]",noise,0.9,body_zone,body_like,short_fragment,False,False
21,1,number,21 of 35,"[1061, 103, 1121, 123]",noise,0.9,"[""page number label""]",noise,0.9,,reference_like,reference_numeric_dot,False,False
21,2,text,"form of PLA-based hydrogels, proves invaluable. Through strategic integration with other polymers, PLA-based hydrogels continue to play essential roles in the field of cartilage tissue engineering.","[326, 175, 1123, 253]",body_paragraph,0.6,"[""default body_paragraph for text label"", ""promoted in tail spread from body_paragraph""]",body_paragraph,0.6,body_zone,body_like,none,True,True
21,3,paragraph_title,4.2.4. PVA-Based Hydrogel,"[328, 271, 577, 295]",subsection_heading,0.85,"[""paragraph_title label with numbering: 4.2.4. PVA-Based Hydrogel""]",subsection_heading,0.85,body_zone,heading_like,heading_numbered,True,True
21,4,text,"Poly-vinyl alcohol (PVA) is created through the alkaline hydrolysis of poly-vinyl acetate, demonstrating notable properties, such as exceptional biocompatibility, suitable biodegradation rate, and non","[324, 302, 1125, 504]",body_paragraph,0.6,"[""default body_paragraph for text label"", ""promoted in tail spread from body_paragraph""]",body_paragraph,0.6,body_zone,body_like,none,True,True
21,5,text,"PVA-based hydrogels, with biomimetic mechanical properties resembling natural cartilage, form colloidal dispersions with 3D network structures through either physical or chemical crosslinking. These h","[323, 503, 1125, 1260]",body_paragraph,0.6,"[""default body_paragraph for text label"", ""promoted in tail spread from body_paragraph""]",body_paragraph,0.6,body_zone,body_like,none,True,True
21,6,text,"Looking ahead, the evolution of PVA hydrogels may progress towards a smart and patient-specific direction. Innovative preparation methods, such as 3D printing and in situ gelation have the potential t","[324, 1256, 1125, 1511]",body_paragraph,0.6,"[""default body_paragraph for text label"", ""promoted in tail spread from body_paragraph""]",body_paragraph,0.6,body_zone,body_like,none,True,True
22,0,header,"Gels 2024, 10, 430","[67, 99, 195, 119]",noise,0.9,"[""header label""]",noise,0.9,body_zone,body_like,short_fragment,False,False
22,1,number,22 of 35,"[1061, 103, 1122, 123]",noise,0.9,"[""page number label""]",noise,0.9,,reference_like,reference_numeric_dot,False,False
22,2,paragraph_title,4.2.5. HAMA-Based Hydrogel,"[327, 175, 605, 201]",subsection_heading,0.85,"[""paragraph_title label with numbering: 4.2.5. HAMA-Based Hydrogel""]",subsection_heading,0.85,body_zone,heading_like,heading_numbered,True,True
22,3,text,"HA is a type of GAG widely present in various tissues, especially in cartilage [181,182]. Although pure HA plays a crucial role in the cartilage matrix by inhibiting close mesenchymal cell–cell intera","[324, 207, 1125, 507]",body_paragraph,0.6,"[""default body_paragraph for text label"", ""promoted in tail spread from body_paragraph""]",body_paragraph,0.6,body_zone,body_like,none,True,True
22,4,text,The creation of a hydrogel from HAMA involves the covalent crosslinking of the methacrylate polymers. Radical polymerization of the polymer-bound (meth) acrylate groups can be initiated either thermal,"[323, 507, 1126, 1465]",body_paragraph,0.6,"[""default body_paragraph for text label"", ""promoted in tail spread from body_paragraph""]",body_paragraph,0.6,body_zone,body_like,none,True,True
22,5,text,"Boasting a swift photosensitive response, rapid gelation speed, and consistent hydrogel performance, the HAMA hydrogel emerges as a viable 3D printable biomaterial suitable for crafting customized sca","[325, 1462, 1124, 1540]",body_paragraph,0.6,"[""default body_paragraph for text label"", ""promoted in tail spread from body_paragraph""]",body_paragraph,0.6,body_zone,body_like,none,True,True
23,0,header,"Gels 2024, 10, 430","[67, 99, 195, 119]",noise,0.9,"[""header label""]",noise,0.9,body_zone,body_like,short_fragment,False,False
23,1,header,23 of 35,"[1061, 103, 1121, 123]",noise,0.9,"[""header label""]",noise,0.9,,reference_like,reference_numeric_dot,False,False
23,2,text,"presence of HA in HAMA bestows it with significant potential to foster chondrogenesis, ultimately contributing to enhanced repair of cartilage by orchestrating the regulation of chondrogenic genes and","[324, 176, 1125, 429]",body_paragraph,0.6,"[""default body_paragraph for text label"", ""promoted in tail spread from body_paragraph""]",body_paragraph,0.6,body_zone,body_like,none,True,True
23,3,paragraph_title,4.2.6. Methylcellulose-Based Hydrogel,"[328, 447, 678, 472]",subsection_heading,0.85,"[""paragraph_title label with numbering: 4.2.6. Methylcellulose-Based Hydrogel""]",subsection_heading,0.85,body_zone,heading_like,heading_numbered,True,True
23,4,text,"In recent years, cellulose (a natural polysaccharide) has garnered significant attention due to its notable properties, including excellent gel formation, non-toxicity, high biocompatibility, biodegra","[324, 478, 1125, 957]",body_paragraph,0.6,"[""default body_paragraph for text label"", ""promoted in tail spread from body_paragraph""]",body_paragraph,0.6,body_zone,body_like,none,True,True
23,5,text,"MC exhibits exceptional thermosensitive gelation characteristics, rheological attributes, non-toxicity, biocompatibility, and a structurally uncomplicated nature. It functions as an inherently functio","[325, 956, 1125, 1534]",body_paragraph,0.6,"[""default body_paragraph for text label"", ""promoted in tail spread from body_paragraph""]",body_paragraph,0.6,body_zone,body_like,none,True,True
24,0,header,"Gels 2024, 10, 430","[67, 99, 195, 119]",noise,0.9,"[""header label""]",noise,0.9,body_zone,body_like,short_fragment,False,False
24,1,number,24 of 35,"[1061, 103, 1121, 123]",noise,0.9,"[""page number label""]",noise,0.9,,reference_like,reference_numeric_dot,False,False
24,2,text,"mechanical stimulation, the MC-based hydrogel serves as a suitable vehicle for MSC retention, fostering load-induced chondrogenesis while mitigating the hypertrophic effects of chondrocytes [200]. Fur","[325, 176, 1125, 502]",body_paragraph,0.6,"[""default body_paragraph for text label"", ""promoted in tail spread from body_paragraph""]",body_paragraph,0.6,body_zone,body_like,none,True,True
24,3,text,"In summary, it has been demonstrated that synthesizing MC-based hydrogels is relatively straightforward, and their mechano-rheological behavior is highly adjustable by manipulating the concentration o","[325, 502, 1124, 754]",body_paragraph,0.6,"[""default body_paragraph for text label"", ""promoted in tail spread from body_paragraph""]",body_paragraph,0.6,body_zone,body_like,none,True,True
24,4,paragraph_title,4.2.7. Polyurethane-Based Hydrogel,"[328, 773, 656, 798]",subsection_heading,0.85,"[""paragraph_title label with numbering: 4.2.7. Polyurethane-Based Hydrogel""]",subsection_heading,0.85,body_zone,heading_like,heading_numbered,True,True
24,5,text,Enhancing the field of regenerative medicine for cartilage repair relies on the utilization of qualified biomaterials capable of restoring or repairing damaged bodily tissue while withstanding signifi,"[326, 805, 1125, 1030]",body_paragraph,0.6,"[""default body_paragraph for text label"", ""promoted in tail spread from body_paragraph""]",body_paragraph,0.6,body_zone,body_like,none,True,True
24,6,text,"Bayer and colleagues first synthesized PU in the 1930s [204]. Typically, PU is synthesized through a polyaddition reaction involving diisocyanates and diols. Under catalyst-free conditions, the alcoho","[326, 1030, 1126, 1534]",body_paragraph,0.6,"[""default body_paragraph for text label"", ""promoted in tail spread from body_paragraph""]",body_paragraph,0.6,body_zone,body_like,none,True,True
25,0,header,"Gels 2024, 10, 430","[67, 99, 195, 119]",noise,0.9,"[""header label""]",noise,0.9,body_zone,body_like,short_fragment,False,False
25,1,header,25 of 35,"[1061, 103, 1121, 123]",noise,0.9,"[""header label""]",noise,0.9,,reference_like,reference_numeric_dot,False,False
25,2,text,"the cells seeded in the scaffolds successfully produced a cartilage-specific ECM, indicating the material's significant potential for cartilage regeneration [209]. Shie MY et al. developed water-based","[326, 176, 1124, 376]",body_paragraph,0.6,"[""default body_paragraph for text label"", ""promoted in tail spread from body_paragraph""]",body_paragraph,0.6,body_zone,body_like,none,True,True
25,3,text,"Further, Chen YW et al. demonstrated through experimental design that they explored the optimal parameters of a suitable biological scaffold modified with light-curing materials, which could facilitat","[325, 377, 1124, 527]",body_paragraph,0.6,"[""default body_paragraph for text label"", ""promoted in tail spread from body_paragraph""]",body_paragraph,0.6,body_zone,body_like,none,True,True
25,4,text,"Beyond 3D bioprinting applications, the direct biological interactions between chondrocytes and PU hydrogel have been already explored. Grad et al. seeded calve-derived chondrocytes in the PU scaffold","[324, 527, 1125, 803]",body_paragraph,0.6,"[""default body_paragraph for text label"", ""promoted in tail spread from body_paragraph""]",body_paragraph,0.6,body_zone,body_like,none,True,True
25,5,text,"As stated above, PU stands out among synthetic and natural biodegradable polymers for its exceptional mechanical and physical properties, which align with human tissues. However, the consideration of ","[325, 803, 1125, 1082]",body_paragraph,0.6,"[""default body_paragraph for text label"", ""promoted in tail spread from body_paragraph""]",body_paragraph,0.6,body_zone,body_like,none,True,True
25,6,paragraph_title,"5. Conclusions, Challenges and Prospects","[328, 1100, 714, 1124]",section_heading,0.85,"[""paragraph_title label with numbering: 5. Conclusions, Challenges and Prospects""]",section_heading,0.85,body_zone,heading_like,heading_numbered,True,True
25,7,text,"Three-dimensional bioprinting technology has been widely used in the study of articular cartilage repair or regeneration. Hydrogels, both naturally derived and chemically synthesized, can be optimized","[326, 1131, 1124, 1408]",body_paragraph,0.6,"[""default body_paragraph for text label"", ""promoted in tail spread from body_paragraph""]",body_paragraph,0.6,body_zone,body_like,none,True,True
25,8,text,"Three-dimensional bioprinting technology has emerged with unique advantages for printing biocompatible materials, cells, and other compounds in 3D constructs for cartilage repair or regeneration. Up t","[325, 1407, 1125, 1535]",body_paragraph,0.6,"[""default body_paragraph for text label"", ""promoted in tail spread from body_paragraph""]",body_paragraph,0.6,body_zone,body_like,none,True,True
26,0,header,"Gels 2024, 10, 430","[67, 99, 195, 119]",noise,0.9,"[""header label""]",noise,0.9,tail_nonref_hold_zone,unknown_like,short_fragment,False,False
26,1,header,26 of 35,"[1061, 103, 1121, 123]",noise,0.9,"[""header label""]",noise,0.9,,reference_like,reference_numeric_dot,False,False
26,2,abstract,"each individual's anatomy. Therefore, 3D-bioprinted cartilage holds great significance in the research and application for cartilage defect repair and regeneration.","[327, 175, 1123, 226]",abstract_body,0.85,"[""abstract label from Paddle OCR""]",abstract_body,0.85,tail_nonref_hold_zone,body_like,none,True,True
26,3,abstract,"Three-dimensional bioprinting has the potential to produce bioprinted constructs capable of cell growth, proliferation, and differentiation, along with controllable mechanical and physical properties.","[325, 227, 1125, 604]",abstract_body,0.85,"[""abstract label from Paddle OCR""]",abstract_body,0.85,tail_nonref_hold_zone,body_like,none,True,True
26,4,abstract,Another issue that needs to be faced is the hypertrophy of chondrocytes or tissue fibrosis during cartilage tissue repair. In order to prevent the hypertrophy or fibrosis of the chondrocytes produced ,"[326, 603, 1125, 855]",abstract_body,0.85,"[""abstract label from Paddle OCR""]",abstract_body,0.85,tail_nonref_hold_zone,body_like,none,True,True
26,5,abstract,"Three-dimensional bioprinting is a promising approach and is considered as a vital tool in the repair or regeneration of cartilage tissue, thus holding the promise to improve the quality of life for p","[326, 855, 1125, 1209]",abstract_body,0.85,"[""abstract label from Paddle OCR""]",abstract_body,0.85,tail_nonref_hold_zone,body_like,none,True,True
26,6,abstract,"Funding: This work was supported by Health@InnoHK, Innovation and Technology Commission, the Re-search Grant Council (GRF14121815), Hong Kong SAR, the Shenzhen Virtue University Park La-boratory Suppo","[327, 1227, 1124, 1324]",abstract_body,0.85,"[""abstract label from Paddle OCR""]",abstract_body,0.85,tail_nonref_hold_zone,unknown_like,none,True,True
26,7,text,"Acknowledgments: We are grateful for the support from the Core Laboratories at the School of Biomedical Sciences, Faculty of Medicine, The Chinese University of Hong Kong.","[327, 1334, 1123, 1383]",body_paragraph,0.6,"[""default body_paragraph for text label"", ""tail_nonref_hold_zone excluded from body flow"", ""tail_nonref_hold_zone excluded from body flow""]",backmatter_body,0.6,tail_nonref_hold_zone,unknown_like,none,True,True
26,8,text,Conflicts of Interest: The authors declare no conflict of interest.,"[328, 1393, 840, 1416]",body_paragraph,0.6,"[""default body_paragraph for text label"", ""tail_nonref_hold_zone excluded from body flow"", ""tail_nonref_hold_zone excluded from body flow""]",backmatter_body,0.6,tail_nonref_hold_zone,support_like,none,True,True
27,0,header,"Gels 2024, 10, 430","[67, 99, 195, 118]",noise,0.9,"[""header label""]",noise,0.9,body_zone,body_like,short_fragment,False,False
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27,2,paragraph_title,References,"[67, 176, 175, 199]",reference_heading,0.9,"[""references heading: References""]",reference_heading,0.9,reference_zone,heading_like,short_fragment,True,True
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29,8,reference_content,"62. Michel, R.; Auzély-Velty, R. Hydrogel-Colloid Composite Bioinks for Targeted Tissue Printing. Biomacromolecules 2020, 21, 2949–2965. [CrossRef]","[68, 477, 1124, 519]",reference_item,0.85,"[""reference content label: 62. Michel, R.; Auz\u00e9ly-Velty, R. Hydrogel-Colloid Composite ""]",reference_item,0.85,reference_zone,reference_like,reference_numeric_dot,True,True
29,9,reference_content,"63. Murab, S.; Gupta, A.; Włodarczyk-Biegun, M.K.; Kumar, A.; van Rijn, P.; Whitlock, P.; Han, S.S.; Agrawal, G. Alginate Based Hydrogel Inks for 3D Bioprinting of Engineered Orthopedic Tissues. Carbo","[67, 522, 1123, 567]",reference_item,0.85,"[""reference content label: 63. Murab, S.; Gupta, A.; W\u0142odarczyk-Biegun, M.K.; Kumar, A.""]",reference_item,0.85,reference_zone,reference_like,reference_numeric_dot,True,True
29,10,reference_content,"64. Hauptstein, J.; Böck, T.; Bartolf-Kopp, M.; Forster, L.; Stahlhut, P.; Nadernezhad, A.; Blahetek, G.; Zernecke-Madsen, A.; Detsch, R.; Jüngst, T.; et al. Hyaluronic Acid-Based Bioink Composition E","[67, 569, 1123, 635]",reference_item,0.85,"[""reference content label: 64. Hauptstein, J.; B\u00f6ck, T.; Bartolf-Kopp, M.; Forster, L.;""]",reference_item,0.85,reference_zone,reference_like,reference_numeric_dot,True,True
29,11,reference_content,"65. Yang, X.; Lu, Z.; Wu, H.; Li, W.; Zheng, L.; Zhao, J. Collagen-alginate as Bioink for Three-dimensional (3D) Cell Printing Based Cartilage Tissue Engineering. Mater. Sci. Eng. C Mater. Biol. Appl.","[67, 637, 1123, 682]",reference_item,0.85,"[""reference content label: 65. Yang, X.; Lu, Z.; Wu, H.; Li, W.; Zheng, L.; Zhao, J. Co""]",reference_item,0.85,reference_zone,reference_like,reference_numeric_dot,True,True
29,12,reference_content,"66. Ni, T.; Liu, M.; Zhang, Y.; Cao, Y.; Pei, R. 3D Bioprinting of Bone Marrow Mesenchymal Stem Cell-Laden Silk Fibroin Double Network Scaffolds for Cartilage Tissue Repair. Bioconjug. Chem. 2020, 31,","[67, 684, 1122, 728]",reference_item,0.85,"[""reference content label: 66. Ni, T.; Liu, M.; Zhang, Y.; Cao, Y.; Pei, R. 3D Bioprint""]",reference_item,0.85,reference_zone,reference_like,reference_numeric_dot,True,True
29,13,reference_content,"67. Wang, J.; Zhang, F.; Tsang, W.P.; Wan, C.; Wu, C. Fabrication of Injectable High Strength Hydrogel Based on 4-arm Star PEG For Cartilage Tissue Engineering. Biomaterials 2017, 120, 11–21. [CrossRe","[67, 730, 1123, 774]",reference_item,0.85,"[""reference content label: 67. Wang, J.; Zhang, F.; Tsang, W.P.; Wan, C.; Wu, C. Fabric""]",reference_item,0.85,reference_zone,reference_like,reference_numeric_dot,True,True
29,14,reference_content,"68. Hölzl, K.; Fürsatz, M.; Göcerler, H.; Schädl, B.; Zigon-Branc, S.; Markovic, M.; Gahleitner, C.; Hoorick, J.V.; Van Vlierberghe, S.; Kleiner, A.; et al. Gelatin Methacryloyl as Environment for Cho","[67, 776, 1123, 842]",reference_item,0.85,"[""reference content label: 68. H\u00f6lzl, K.; F\u00fcrsatz, M.; G\u00f6cerler, H.; Sch\u00e4dl, B.; Zigon-""]",reference_item,0.85,reference_zone,reference_like,reference_numeric_dot,True,True
29,15,reference_content,"69. Rosenzweig, D.H.; Carelli, E.; Steffen, T.; Jarzem, P.; Haglund, L. 3D-Printed ABS and PLA Scaffolds for Cartilage and Nucleus Pulposus Tissue Regeneration. Int. J. Mol. Sci. 2015, 16, 15118–15135","[66, 845, 1123, 889]",reference_item,0.85,"[""reference content label: 69. Rosenzweig, D.H.; Carelli, E.; Steffen, T.; Jarzem, P.; ""]",reference_item,0.85,reference_zone,reference_like,reference_numeric_dot,True,True
29,16,reference_content,"70. Setayeshmehr, M.; Hafeez, S.; van Blitterswijk, C.; Moroni, L.; Mota, C.; Baker, M.B. Bioprinting Via a Dual-Gel Bioink Based on Poly(Vinyl Alcohol) and Solubilized Extracellular Matrix towards Ca","[66, 891, 1123, 936]",reference_item,0.85,"[""reference content label: 70. Setayeshmehr, M.; Hafeez, S.; van Blitterswijk, C.; Moro""]",reference_item,0.85,reference_zone,reference_like,reference_numeric_dot,True,True
29,17,reference_content,"71. Ma, K.; Zhao, T.; Yang, L.; Wang, P.; Jin, J.; Teng, H.; Xia, D.; Zhu, L.; Li, L.; Jiang, Q.; et al. Application of Robotic-assisted in Situ 3D Printing in Cartilage Regeneration with HAMA Hydroge","[66, 937, 1122, 1003]",reference_item,0.85,"[""reference content label: 71. Ma, K.; Zhao, T.; Yang, L.; Wang, P.; Jin, J.; Teng, H.;""]",reference_item,0.85,reference_zone,reference_like,reference_numeric_dot,True,True
29,18,reference_content,"72. Tønnesen, H.H.; Karlsen, J. Alginate in Drug Delivery Systems. Drug Dev. Ind. Pharm. 2002, 28, 621–630. [CrossRef]","[65, 1005, 1029, 1028]",reference_item,0.85,"[""reference content label: 72. T\u00f8nnesen, H.H.; Karlsen, J. Alginate in Drug Delivery Sy""]",reference_item,0.85,reference_zone,reference_like,reference_numeric_dot,True,True
29,19,reference_content,"73. Ruvinov, E.; Cohen, S. Alginate Biomaterial for the Treatment of Myocardial Infarction: Progress, Translational Strategies, and Clinical Outlook: From Ocean Algae to Patient Bedside. Adv. Drug Del","[67, 1030, 1120, 1072]",reference_item,0.85,"[""reference content label: 73. Ruvinov, E.; Cohen, S. Alginate Biomaterial for the Trea""]",reference_item,0.85,reference_zone,reference_like,reference_numeric_dot,True,True
29,20,reference_content,"74. Axpe, E.; Oyen, M.L. Applications of Alginate-Based Bioinks in 3D Bioprinting. Int. J. Mol. Sci. 2016, 17, 1976. [CrossRef]","[65, 1074, 1075, 1097]",reference_item,0.85,"[""reference content label: 74. Axpe, E.; Oyen, M.L. Applications of Alginate-Based Bioi""]",reference_item,0.85,reference_zone,reference_like,reference_numeric_dot,True,True
29,21,reference_content,"75. Hadley, D.J.; Silva, E.A. Thaw-Induced Gelation of Alginate Hydrogels for Versatile Delivery of Therapeutics. Ann. Biomed. Eng. 2019, 47, 1701–1710. [CrossRef]","[67, 1098, 1123, 1140]",reference_item,0.85,"[""reference content label: 75. Hadley, D.J.; Silva, E.A. Thaw-Induced Gelation of Algin""]",reference_item,0.85,reference_zone,reference_like,reference_numeric_dot,True,True
29,22,reference_content,"76. Catoira, M.C.; Fusaro, L.; Di Francesco, D.; Ramella, M.; Boccafoschi, F. Overview of Natural Hydrogels for Regenerative Medicine Applications. J. Mater. Sci. Mater. Med. 2019, 30, 115. [CrossRef]","[66, 1143, 1121, 1188]",reference_item,0.85,"[""reference content label: 76. Catoira, M.C.; Fusaro, L.; Di Francesco, D.; Ramella, M.""]",reference_item,0.85,reference_zone,reference_like,reference_numeric_dot,True,True
29,23,reference_content,"77. Öztürk, E.; Stauber, T.; Levinson, C.; Cavalli, E.; Arlov, Ø.; Zenobi-Wong, M. Tyrosinase-Crosslinked, Tissue Adhesive and Biomimetic Alginate Sulfate Hydrogels for Cartilage Repair. Biomed. Mater","[66, 1190, 1122, 1234]",reference_item,0.85,"[""reference content label: 77. \u00d6zt\u00fcrk, E.; Stauber, T.; Levinson, C.; Cavalli, E.; Arlo""]",reference_item,0.85,reference_zone,reference_like,reference_numeric_dot,True,True
29,24,reference_content,"78. Hontani, K.; Onodera, T.; Terashima, M.; Momma, D.; Matsuoka, M.; Baba, R.; Joutoku, Z.; Matsubara, S.; Homan, K.; Hishimura, R.; et al. Chondrogenic Differentiation of Mouse Induced Pluripotent S","[68, 1236, 1122, 1304]",reference_item,0.85,"[""reference content label: 78. Hontani, K.; Onodera, T.; Terashima, M.; Momma, D.; Mats""]",reference_item,0.85,reference_zone,reference_like,reference_numeric_dot,True,True
29,25,reference_content,"79. Mahmoudi, Z.; Mohammadnejad, J.; Razavi Bazaz, S.; Abouei Mehrizi, A.; Saidijam, M.; Dinarvand, R.; Ebrahimi Warkiani, M.; Soleimani, M. Promoted Chondrogenesis of hMCSs with Controlled Release of","[67, 1306, 1124, 1372]",reference_item,0.85,"[""reference content label: 79. Mahmoudi, Z.; Mohammadnejad, J.; Razavi Bazaz, S.; Aboue""]",reference_item,0.85,reference_zone,reference_like,reference_numeric_dot,True,True
29,26,reference_content,"80. Balakrishnan, B.; Joshi, N.; Jayakrishnan, A.; Banerjee, R. Self-Crosslinked Oxidized Alginate/Gelatin Hydrogel as Injectable, Adhesive Biomimetic Scaffolds for Cartilage Regeneration. Acta. Bioma","[66, 1375, 1123, 1419]",reference_item,0.85,"[""reference content label: 80. Balakrishnan, B.; Joshi, N.; Jayakrishnan, A.; Banerjee,""]",reference_item,0.85,reference_zone,reference_like,reference_numeric_dot,True,True
29,27,reference_content,"81. Kundu, J.; Shim, J.H.; Jang, J.; Kim, S.W.; Cho, D.W. An Additive Manufacturing-Based PCL-Alginate-Chondrocyte Bioprinted Scaffold for Cartilage Tissue Engineering. J. Tissue Eng. Regen. Med. 2015","[66, 1422, 1122, 1465]",reference_item,0.85,"[""reference content label: 81. Kundu, J.; Shim, J.H.; Jang, J.; Kim, S.W.; Cho, D.W. An""]",reference_item,0.85,reference_zone,reference_like,reference_numeric_dot,True,True
29,28,reference_content,"82. Kosik-Kozioł, A.; Costantini, M.; Bolek, T.; Szöke, K.; Barbetta, A.; Brinchmann, J.; Święszkowski, W. PLA Short Sub-Micron Fiber Reinforcement of 3D Bioprinted Alginate Constructs for Cartilage R","[67, 1467, 1123, 1534]",reference_item,0.85,"[""reference content label: 82. Kosik-Kozio\u0142, A.; Costantini, M.; Bolek, T.; Sz\u00f6ke, K.; ""]",reference_item,0.85,reference_zone,reference_like,reference_numeric_dot,True,True
30,0,header,"Gels 2024, 10, 430","[67, 99, 195, 119]",noise,0.9,"[""header label""]",noise,0.9,,unknown_like,short_fragment,False,False
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30,2,reference_content,"83. Kilian, D.; Ahlfeld, T.; Akkineni, A.R.; Bernhardt, A.; Gelinsky, M.; Lode, A. 3D Bioprinting of Osteochondral Tissue Substitutes—in Vitro-Chondrogenesis in Multi-Layered Mineralized Constructs. S","[65, 177, 1121, 222]",reference_item,0.85,"[""reference content label: 83. Kilian, D.; Ahlfeld, T.; Akkineni, A.R.; Bernhardt, A.; ""]",reference_item,0.85,reference_zone,reference_like,reference_numeric_dot,True,True
30,3,reference_content,"84. Olate-Moya, F.; Arens, L.; Wilhelm, M.; Mateos-Timoneda, M.A.; Engel, E.; Palza, H. Chondroinductive Alginate-Based Hydrogels Having Graphene Oxide for 3D Printed Scaffold Fabrication. ACS Appl. M","[66, 224, 1123, 267]",reference_item,0.85,"[""reference content label: 84. Olate-Moya, F.; Arens, L.; Wilhelm, M.; Mateos-Timoneda,""]",reference_item,0.85,reference_zone,reference_like,reference_numeric_dot,True,True
30,4,reference_content,"85. Schwarz, S.; Kuth, S.; Distler, T.; Gögele, C.; Stölzel, K.; Detsch, R.; Boccaccini, A.R.; Schulze-Tanzil, G. 3D Printing and Characterization of Human Nasoseptal Chondrocytes Laden Dual Crosslink","[67, 269, 1122, 337]",reference_item,0.85,"[""reference content label: 85. Schwarz, S.; Kuth, S.; Distler, T.; G\u00f6gele, C.; St\u00f6lzel,""]",reference_item,0.85,reference_zone,reference_like,reference_numeric_dot,True,True
30,5,reference_content,"86. Yu, X.; Deng, Z.; Li, H.; Ma, Y.; Zheng, Q. In situ Fabrication of an Anisotropic Double-Layer Hydrogel as a Bio-Scaffold for Repairing Articular Cartilage and Subchondral Bone Injuries. RSC Adv. ","[67, 339, 1123, 383]",reference_item,0.85,"[""reference content label: 86. Yu, X.; Deng, Z.; Li, H.; Ma, Y.; Zheng, Q. In situ Fabr""]",reference_item,0.85,reference_zone,reference_like,reference_numeric_dot,True,True
30,6,reference_content,"87. Laporte, C.; Tubbs, E.; Pierron, M.; Gallego, A.; Moisan, A.; Lamarche, F.; Lozano, T.; Hernandez, A.; Cottet-Rousselle, C.; Gauchez, A.S.; et al. Improved Human Islets’ Viability and Functionalit","[67, 385, 1123, 473]",reference_item,0.85,"[""reference content label: 87. Laporte, C.; Tubbs, E.; Pierron, M.; Gallego, A.; Moisan""]",reference_item,0.85,reference_zone,reference_like,reference_numeric_dot,True,True
30,7,reference_content,"88. Wang, H.; Yin, R.; Chen, X.; Wu, T.; Bu, Y.; Yan, H.; Lin, Q. Construction and Evaluation of Alginate Dialdehyde Grafted RGD Derivatives/Polyvinyl Alcohol/Cellulose Nanocrystals IPN Composite Hydr","[66, 477, 1123, 519]",reference_item,0.85,"[""reference content label: 88. Wang, H.; Yin, R.; Chen, X.; Wu, T.; Bu, Y.; Yan, H.; Li""]",reference_item,0.85,reference_zone,reference_like,reference_numeric_dot,True,True
30,8,reference_content,"89. Dumbleton, J.; Shamul, J.G.; Jiang, B.; Agarwal, P.; Huang, H.; Jia, X.; He, X. Oxidation and RGD Modification Affect the Early Neural Differentiation of Murine Embryonic Stem Cells Cultured in Co","[67, 523, 1123, 589]",reference_item,0.85,"[""reference content label: 89. Dumbleton, J.; Shamul, J.G.; Jiang, B.; Agarwal, P.; Hua""]",reference_item,0.85,reference_zone,reference_like,reference_numeric_dot,True,True
30,9,reference_content,"90. Lertwimol, T.; Sonthithai, P.; Hankamolsiri, W.; Kaewkong, P.; Uppanan, P. Development of Chondrocyte-Laden Alginate Hydrogels with Modulated Microstructure and Properties for Cartilage Regenerati","[67, 592, 1123, 636]",reference_item,0.85,"[""reference content label: 90. Lertwimol, T.; Sonthithai, P.; Hankamolsiri, W.; Kaewkon""]",reference_item,0.85,reference_zone,reference_like,reference_numeric_dot,True,True
30,10,reference_content,"91. Morshedloo, F.; Khoshfetrat, A.B.; Kazemi, D.; Ahmadian, M. Gelatin Improves Peroxidase-Mediated Alginate Hydrogel Characteristics as a Potential Injectable Hydrogel for Soft Tissue Engineering Ap","[67, 638, 1123, 704]",reference_item,0.85,"[""reference content label: 91. Morshedloo, F.; Khoshfetrat, A.B.; Kazemi, D.; Ahmadian,""]",reference_item,0.85,reference_zone,reference_like,reference_numeric_dot,True,True
30,11,reference_content,"92. Haung, S.M.; Lin, Y.T.; Liu, S.M.; Chen, J.C.; Chen, W.C. In Vitro Evaluation of a Composite Gelatin-Hyaluronic Acid-Alginate Porous Scaffold with Different Pore Distributions for Cartilage Regene","[66, 706, 1122, 751]",reference_item,0.85,"[""reference content label: 92. Haung, S.M.; Lin, Y.T.; Liu, S.M.; Chen, J.C.; Chen, W.C""]",reference_item,0.85,reference_zone,reference_like,reference_numeric_dot,True,True
30,12,reference_content,"93. Migliore, A.; Procopio, S. Effectiveness and Utility of Hyaluronic Acid in Osteoarthritis. Clin. Cases Miner. Bone Metab. 2015, 12, 31–33. [CrossRef]","[67, 753, 1124, 795]",reference_item,0.85,"[""reference content label: 93. Migliore, A.; Procopio, S. Effectiveness and Utility of ""]",reference_item,0.85,reference_zone,reference_like,reference_numeric_dot,True,True
30,13,reference_content,"94. Antich, C.; de Vicente, J.; Jiménez, G.; Chocarro, C.; Carrillo, E.; Montañez, E.; Gálvez-Martín, P.; Marchal, J.A. Bio-inspired Hydrogel Composed of Hyaluronic Acid and Alginate as a Potential Bi","[66, 799, 1122, 865]",reference_item,0.85,"[""reference content label: 94. Antich, C.; de Vicente, J.; Jim\u00e9nez, G.; Chocarro, C.; C""]",reference_item,0.85,reference_zone,reference_like,reference_numeric_dot,True,True
30,14,reference_content,"95. Chen, H.; Xue, H.; Zeng, H.; Dai, M.; Tang, C.; Liu, L. 3D Printed Scaffolds Based on Hyaluronic Acid Bioinks for Tissue Engineering: A Review. Biomater. Res. 2023, 27, 137. [CrossRef] [PubMed]","[66, 868, 1122, 912]",reference_item,0.85,"[""reference content label: 95. Chen, H.; Xue, H.; Zeng, H.; Dai, M.; Tang, C.; Liu, L. ""]",reference_item,0.85,reference_zone,reference_like,reference_numeric_dot,True,True
30,15,reference_content,"96. Wan, T.; Fan, P.; Zhang, M.; Shi, K.; Chen, X.; Yang, H.; Liu, X.; Xu, W.; Zhou, Y. Multiple Crosslinking Hyaluronic Acid Hydrogels with Improved Strength and 3D Printability. ACS Appl. Bio. Mater","[66, 914, 1122, 959]",reference_item,0.85,"[""reference content label: 96. Wan, T.; Fan, P.; Zhang, M.; Shi, K.; Chen, X.; Yang, H.""]",reference_item,0.85,reference_zone,reference_like,reference_numeric_dot,True,True
30,16,reference_content,"97. Abatangelo, G.; Vindigni, V.; Avruscio, G.; Pandis, L.; Brun, P. Hyaluronic Acid: Redefining its Role. Cells 2020, 9, 1743. [CrossRef]","[66, 959, 1120, 982]",reference_item,0.85,"[""reference content label: 97. Abatangelo, G.; Vindigni, V.; Avruscio, G.; Pandis, L.; ""]",reference_item,0.85,reference_zone,reference_like,reference_numeric_dot,True,True
30,17,reference_content,"98. Tsanaktsidou, E.; Kammona, O.; Kiparissides, C. On The Synthesis and Characterization of Biofunctional Hyaluronic Acid Based Injectable Hydrogels for The Repair of Cartilage Lesions. Eur. Polym. J","[65, 982, 1122, 1026]",reference_item,0.85,"[""reference content label: 98. Tsanaktsidou, E.; Kammona, O.; Kiparissides, C. On The S""]",reference_item,0.85,reference_zone,reference_like,reference_numeric_dot,True,True
30,18,reference_content,"99. Hong, B.M.; Kim, H.C.; Jeong, J.E.; Park, S.A.; Park, W.H. Visible-Light-Induced Hyaluronate Hydrogel for Soft tissue Fillers. Int. J. Biol. Macromol. 2020, 165, 2834–2844. [CrossRef]","[67, 1029, 1123, 1073]",reference_item,0.85,"[""reference content label: 99. Hong, B.M.; Kim, H.C.; Jeong, J.E.; Park, S.A.; Park, W.""]",reference_item,0.85,reference_zone,reference_like,reference_numeric_dot,True,True
30,19,reference_content,"100. Wang, H.; Xu, Y.; Wang, P.; Ma, J.; Wang, P.; Han, X.; Fan, Y.; Bai, D.; Sun, Y.; Zhang, X. Cell-Mediated Injectable Blend Hydrogel-BCP Ceramic Scaffold for In Situ Condylar Osteochondral Repair.","[67, 1075, 1122, 1119]",reference_item,0.85,"[""reference content label: 100. Wang, H.; Xu, Y.; Wang, P.; Ma, J.; Wang, P.; Han, X.; ""]",reference_item,0.85,reference_zone,reference_like,reference_numeric_dot,True,True
30,20,reference_content,"101. Shokri, A.; Ramezani, K.; Jamalpour, M.R.; Mohammadi, C.; Vahdatinia, F.; Irani, A.D.; Sharifi, E.; Haddadi, R.; Jamshidi, S.; Amirabad, L.M. In Vivo Efficacy of 3D-Printed Elastin–Gelatin–Hyalur","[68, 1121, 1124, 1188]",reference_item,0.85,"[""reference content label: 101. Shokri, A.; Ramezani, K.; Jamalpour, M.R.; Mohammadi, C""]",reference_item,0.85,reference_zone,reference_like,reference_numeric_dot,True,True
30,21,reference_content,"102. Shi, W.; Fang, F.; Kong, Y.; Greer, S.E.; Kuss, M.; Liu, B.; Xue, W.; Jiang, X.; Lovell, P.; Mohs, A.M.; et al. Dynamic Hyaluronic Acid Hydrogel with Covalent Linked Gelatin as an Anti-oxidative ","[68, 1190, 1123, 1256]",reference_item,0.85,"[""reference content label: 102. Shi, W.; Fang, F.; Kong, Y.; Greer, S.E.; Kuss, M.; Liu""]",reference_item,0.85,reference_zone,reference_like,reference_numeric_dot,True,True
30,22,reference_content,"103. Zhu, D.; Wang, H.; Trinh, P.; Heilshorn, S.C.; Yang, F. Elastin-Like Protein-Hyaluronic Acid (ELP-HA) Hydrogels with Decoupled Mechanical and Biochemical Cues for Cartilage Regeneration. Biomater","[71, 1259, 1120, 1304]",reference_item,0.85,"[""reference content label: 103. Zhu, D.; Wang, H.; Trinh, P.; Heilshorn, S.C.; Yang, F.""]",reference_item,0.85,reference_zone,reference_like,reference_numeric_dot,True,True
30,23,reference_content,"104. Lin, H.; Beck, A.M.; Shimomura, K.; Sohn, J.; Fritch, M.R.; Deng, Y.; Kilroy, E.J.; Tang, Y.; Alexander, P.G.; Tuan, R.S. Optimization of Photocrosslinked Gelatin/Hyaluronic Acid Hybrid Scaffold ","[68, 1306, 1123, 1371]",reference_item,0.85,"[""reference content label: 104. Lin, H.; Beck, A.M.; Shimomura, K.; Sohn, J.; Fritch, M""]",reference_item,0.85,reference_zone,reference_like,reference_numeric_dot,True,True
30,24,reference_content,"105. Titan, A.; Schär, M.; Hutchinson, I.; Demange, M.; Chen, T.; Rodeo, S. Growth Factor Delivery to a Cartilage-Cartilage Interface Using Platelet-Rich Concentrates on a Hyaluronic Acid Scaffold. Ar","[67, 1375, 1122, 1419]",reference_item,0.85,"[""reference content label: 105. Titan, A.; Sch\u00e4r, M.; Hutchinson, I.; Demange, M.; Chen""]",reference_item,0.85,reference_zone,reference_like,reference_numeric_dot,True,True
30,25,reference_content,"106. Depalle, B.; Qin, Z.; Shefelbine, S.J.; Buehler, M.J. Influence of Cross-link Structure, Density and Mechanical Properties in the Mesoscale Deformation Mechanisms of Collagen Fibrils. J. Mech. Be","[67, 1421, 1121, 1465]",reference_item,0.85,"[""reference content label: 106. Depalle, B.; Qin, Z.; Shefelbine, S.J.; Buehler, M.J. I""]",reference_item,0.85,reference_zone,reference_like,reference_numeric_dot,True,True
30,26,reference_content,"107. Marques, C.F.; Diogo, G.S.; Pina, S.; Oliveira, J.M.; Silva, T.H.; Reis, R.L. Collagen-based Bioinks for Hard Tissue Engineering Applications: A Comprehensive Review. J. Mater. Sci. Mater. Med. 2","[67, 1467, 1120, 1512]",reference_item,0.85,"[""reference content label: 107. Marques, C.F.; Diogo, G.S.; Pina, S.; Oliveira, J.M.; S""]",reference_item,0.85,reference_zone,reference_like,reference_numeric_dot,True,True
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31,2,reference_content,"108. Hesse, E.; Hefferan, T.E.; Tarara, J.E.; Haasper, C.; Meller, R.; Krettek, C.; Lu, L.; Yaszemski, M.J. Collagen Type I Hydrogel Allows Migration, Proliferation, and Osteogenic Differentiation of ","[67, 177, 1122, 244]",reference_item,0.85,"[""reference content label: 108. Hesse, E.; Hefferan, T.E.; Tarara, J.E.; Haasper, C.; M""]",reference_item,0.85,reference_zone,reference_like,reference_numeric_dot,True,True
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31,7,reference_content,"113. Lu, Z.; Liu, S.; Le, Y.; Qin, Z.; He, M.; Xu, F.; Zhu, Y.; Zhao, J.; Mao, C.; Zheng, L. An Injectable Collagen-Genipin-Carbon Dot Hydrogel Combined with Photodynamic Therapy to Enhance Chondrogen","[69, 453, 1123, 519]",reference_item,0.85,"[""reference content label: 113. Lu, Z.; Liu, S.; Le, Y.; Qin, Z.; He, M.; Xu, F.; Zhu, ""]",reference_item,0.85,reference_zone,reference_like,reference_numeric_dot,True,True
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31,9,reference_content,"115. Shim, J.H.; Jang, K.M.; Hahn, S.K.; Park, J.Y.; Jung, H.; Oh, K.; Park, K.M.; Yeom, J.; Park, S.H.; Kim, S.W.; et al. Three-dimensional Bioprinting of Multilayered Constructs Containing Human Mes","[68, 592, 1123, 659]",reference_item,0.85,"[""reference content label: 115. Shim, J.H.; Jang, K.M.; Hahn, S.K.; Park, J.Y.; Jung, H""]",reference_item,0.85,reference_zone,reference_like,reference_numeric_dot,True,True
31,10,reference_content,"116. Wang, C.; Yue, H.; Huang, W.; Lin, X.; Xie, X.; He, Z.; He, X.; Liu, S.; Bai, L.; Lu, B.; et al. Cryogenic 3D Printing of Heterogeneous Scaffolds with Gradient Mechanical Strengths and Spatial De","[67, 661, 1123, 728]",reference_item,0.85,"[""reference content label: 116. Wang, C.; Yue, H.; Huang, W.; Lin, X.; Xie, X.; He, Z.;""]",reference_item,0.85,reference_zone,reference_like,reference_numeric_dot,True,True
31,11,reference_content,"117. Yang, Y.; Wang, Z.; Xu, Y.; Xia, J.; Xu, Z.; Zhu, S.; Jin, M. Preparation of Chitosan/Recombinant Human Collagen-Based Photo-Responsive Bioinks for 3D Bioprinting. Gels 2022, 8, 314. [CrossRef]","[67, 729, 1122, 773]",reference_item,0.85,"[""reference content label: 117. Yang, Y.; Wang, Z.; Xu, Y.; Xia, J.; Xu, Z.; Zhu, S.; J""]",reference_item,0.85,reference_zone,reference_like,reference_numeric_dot,True,True
31,12,reference_content,"118. Lan, X.; Ma, Z.; Dimitrov, A.; Kunze, M.; Mulet-Sierra, A.; Ansari, K.; Osswald, M.; Seikaly, H.; Boluk, Y.; Adesida, A.B. Double Crosslinked Hyaluronic Acid and Collagen as a Potential Bioink fo","[68, 776, 1124, 842]",reference_item,0.85,"[""reference content label: 118. Lan, X.; Ma, Z.; Dimitrov, A.; Kunze, M.; Mulet-Sierra,""]",reference_item,0.85,reference_zone,reference_like,reference_numeric_dot,True,True
31,13,reference_content,"119. Li, Y.Y.; Choy, T.H.; Ho, F.C.; Chan, P.B. Scaffold Composition Affects Cytoskeleton Organization, Cell-Matrix Interaction and the Cellular Fate of Human Mesenchymal Stem Cells Upon Chondrogenic ","[68, 845, 1122, 889]",reference_item,0.85,"[""reference content label: 119. Li, Y.Y.; Choy, T.H.; Ho, F.C.; Chan, P.B. Scaffold Com""]",reference_item,0.85,reference_zone,reference_like,reference_numeric_dot,True,True
31,14,reference_content,"120. Buma, P.; Pieper, J.S.; van Tienen, T.; van Susante, J.L.; van der Kraan, P.M.; Veerkamp, J.H.; van den Berg, W.B.; Veth, R.P.; van Kuppevelt, T.H. Cross-linked Type I and Type II Collagenous Mat","[67, 891, 1123, 957]",reference_item,0.85,"[""reference content label: 120. Buma, P.; Pieper, J.S.; van Tienen, T.; van Susante, J.""]",reference_item,0.85,reference_zone,reference_like,reference_numeric_dot,True,True
31,15,reference_content,"121. Yoon, H.J.; Kim, S.B.; Somaiya, D.; Noh, M.J.; Choi, K.B.; Lim, C.L.; Lee, H.Y.; Lee, Y.J.; Yi, Y.; Lee, K.H. Type II Collagen and Glycosaminoglycan Expression Induction in Primary Human Chondroc","[67, 960, 1123, 1026]",reference_item,0.85,"[""reference content label: 121. Yoon, H.J.; Kim, S.B.; Somaiya, D.; Noh, M.J.; Choi, K.""]",reference_item,0.85,reference_zone,reference_like,reference_numeric_dot,True,True
31,16,reference_content,"122. Tiruvannamalai Annamalai, R.; Mertz, D.R.; Daley, E.L.; Stegemann, J.P. Collagen Type II Enhances Chondrogenic Differentiation in Agarose-based Modular Microtissues. Cytotherapy 2016, 18, 263–277","[67, 1029, 1122, 1073]",reference_item,0.85,"[""reference content label: 122. Tiruvannamalai Annamalai, R.; Mertz, D.R.; Daley, E.L.;""]",reference_item,0.85,reference_zone,reference_like,reference_numeric_dot,True,True
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31,24,reference_content,"130. Rodriguez, M.J.; Brown, J.; Giordano, J.; Lin, S.J.; Omenetto, F.G.; Kaplan, D.L. Silk Based Bioinks for Soft Tissue Reconstruction Using 3-Dimensional (3D) Printing with In Vitro and in Vivo Ass","[69, 1398, 1121, 1441]",reference_item,0.85,"[""reference content label: 130. Rodriguez, M.J.; Brown, J.; Giordano, J.; Lin, S.J.; Om""]",reference_item,0.85,reference_zone,reference_like,reference_numeric_dot,True,True
31,25,reference_content,"131. Geão, C.; Costa-Pinto, A.R.; Cunha-Reis, C.; Ribeiro, V.P.; Vieira, S.; Oliveira, J.M.; Reis, R.L.; Oliveira, A.L. Thermal Annealed Silk Fibroin Membranes for Periodontal Guided Tissue Regenerati","[67, 1444, 1123, 1488]",reference_item,0.85,"[""reference content label: 131. Ge\u00e3o, C.; Costa-Pinto, A.R.; Cunha-Reis, C.; Ribeiro, V""]",reference_item,0.85,reference_zone,reference_like,reference_numeric_dot,True,True
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32,3,reference_content,"133. Kulchar, R.J.; Denzer, B.R.; Chavre, B.M.; Takegami, M.; Patterson, J. A Review of the Use of Microparticles for Cartilage Tissue Engineering. Int. J. Mol. Sci. 2021, 22, 10292. [CrossRef] [PubMe","[68, 246, 1122, 291]",reference_item,0.85,"[""reference content label: 133. Kulchar, R.J.; Denzer, B.R.; Chavre, B.M.; Takegami, M.""]",reference_item,0.85,reference_zone,reference_like,reference_numeric_dot,True,True
32,4,reference_content,"134. Wu, T.; Chen, Y.; Liu, W.; Tong, K.L.; Suen, C.-W.W.; Huang, S.; Hou, H.; She, G.; Zhang, H.; Zheng, X. Ginsenoside Rb1/TGF-β1 Loaded Biodegradable Silk Fibroin-Gelatin Porous Scaffolds for Infla","[68, 293, 1123, 360]",reference_item,0.85,"[""reference content label: 134. Wu, T.; Chen, Y.; Liu, W.; Tong, K.L.; Suen, C.-W.W.; H""]",reference_item,0.85,reference_zone,reference_like,reference_numeric_dot,True,True
32,5,reference_content,"135. Wang, T.; Li, Y.; Liu, J.; Fang, Y.; Guo, W.; Liu, Y.; Li, X.; Li, G.; Wang, X.; Zheng, Z. Intraarticularly Injectable Silk Hydrogel Microspheres with Enhanced Mechanical and Structural Stability","[68, 361, 1123, 429]",reference_item,0.85,"[""reference content label: 135. Wang, T.; Li, Y.; Liu, J.; Fang, Y.; Guo, W.; Liu, Y.; ""]",reference_item,0.85,reference_zone,reference_like,reference_numeric_dot,True,True
32,6,reference_content,"136. Shi, W.; Sun, M.; Hu, X.; Ren, B.; Cheng, J.; Li, C.; Duan, X.; Fu, X.; Zhang, J.; Chen, H. Structurally and Functionally Optimized Silk-Fibroin–Gelatin Scaffold Using 3D Printing to Repair Carti","[68, 430, 1122, 496]",reference_item,0.85,"[""reference content label: 136. Shi, W.; Sun, M.; Hu, X.; Ren, B.; Cheng, J.; Li, C.; D""]",reference_item,0.85,reference_zone,reference_like,reference_numeric_dot,True,True
32,7,reference_content,"137. Pan, Z.; Hou, M.; Zhang, Y.; Liu, Y.; Tian, X.; Hu, X.; Ge, X.; Zhao, Z.; Liu, T.; Xu, Y.; et al. Incorporation of Kartogenin and Silk Fibroin Scaffolds Promotes Rat Articular Regeneration throug","[68, 499, 1123, 566]",reference_item,0.85,"[""reference content label: 137. Pan, Z.; Hou, M.; Zhang, Y.; Liu, Y.; Tian, X.; Hu, X.;""]",reference_item,0.85,reference_zone,reference_like,reference_numeric_dot,True,True
32,8,reference_content,"138. Chakraborty, J.; Fernández-Pérez, J.; Van Kampen, K.A.; Roy, S.; ten Brink, T.; Mota, C.; Ghosh, S.; Moroni, L. Development of a Biomimetic Arch-like 3D Bioprinted Construct for Cartilage Regener","[69, 569, 1124, 635]",reference_item,0.85,"[""reference content label: 138. Chakraborty, J.; Fern\u00e1ndez-P\u00e9rez, J.; Van Kampen, K.A.;""]",reference_item,0.85,reference_zone,reference_like,reference_numeric_dot,True,True
32,9,reference_content,"139. Wu, D.; Li, J.; Wang, C.; Su, Z.; Su, H.; Chen, Y.; Yu, B. Injectable Silk Fibroin Peptide Nanofiber Hydrogel Composite Scaffolds for Cartilage Regeneration. Mater. Today Bio. 2024, 25, 100962. [","[68, 638, 1123, 682]",reference_item,0.85,"[""reference content label: 139. Wu, D.; Li, J.; Wang, C.; Su, Z.; Su, H.; Chen, Y.; Yu,""]",reference_item,0.85,reference_zone,reference_like,reference_numeric_dot,True,True
32,10,reference_content,"140. Yan, K.; Zhang, X.; Liu, Y.; Cheng, J.; Zhai, C.; Shen, K.; Liang, W.; Fan, W. 3D-bioprinted silk fibroin-hydroxypropyl cellulose methacrylate porous scaffold with optimized performance for repai","[68, 684, 1123, 749]",reference_item,0.85,"[""reference content label: 140. Yan, K.; Zhang, X.; Liu, Y.; Cheng, J.; Zhai, C.; Shen,""]",reference_item,0.85,reference_zone,reference_like,reference_numeric_dot,True,True
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32,15,reference_content,"145. Herzberger, J.; Niederer, K.; Pohlit, H.; Seiwert, J.; Worm, M.; Wurm, F.R.; Frey, H. Polymerization of Ethylene Oxide, Propylene Oxide, and Other Alkylene Oxides: Synthesis, Novel Polymer Archit","[66, 937, 1123, 1003]",reference_item,0.85,"[""reference content label: 145. Herzberger, J.; Niederer, K.; Pohlit, H.; Seiwert, J.; ""]",reference_item,0.85,reference_zone,reference_like,reference_numeric_dot,True,True
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32,19,reference_content,"149. Fedorovich, N.E.; Oudshoorn, M.H.; van Geemen, D.; Hennink, W.E.; Alblas, J.; Dhert, W.J.A. The Effect of Photopolymerization on Stem Cells Embedded in Hydrogels. Biomaterials 2009, 30, 344–353. ","[71, 1144, 1120, 1188]",reference_item,0.85,"[""reference content label: 149. Fedorovich, N.E.; Oudshoorn, M.H.; van Geemen, D.; Henn""]",reference_item,0.85,reference_zone,reference_like,reference_numeric_dot,True,True
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32,21,reference_content,"151. Kim, J.S.; Choi, J.; Ki, C.S.; Lee, K.H. 3D Silk Fiber Construct Embedded Dual-Layer PEG Hydrogel for Articular Carnage Repair—In vitro Assessment. Front. Bioeng. Biotechnol. 2021, 9, 653509. [Cr","[67, 1237, 1123, 1281]",reference_item,0.85,"[""reference content label: 151. Kim, J.S.; Choi, J.; Ki, C.S.; Lee, K.H. 3D Silk Fiber ""]",reference_item,0.85,reference_zone,reference_like,reference_numeric_dot,True,True
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32,24,reference_content,"154. Mad-Ali, S.; Benjakul, S.; Prodpran, T.; Maqsood, S. Characteristics and Gelling Properties of Gelatin from Goat Skin as Affected by Drying Methods. J. Food Sci. Technol. 2017, 54, 1646–1654. [Cr","[68, 1375, 1120, 1419]",reference_item,0.85,"[""reference content label: 154. Mad-Ali, S.; Benjakul, S.; Prodpran, T.; Maqsood, S. Ch""]",reference_item,0.85,reference_zone,reference_like,heading_numbered,True,True
32,25,reference_content,"155. Van Den Bulcke, A.I.; Bogdanov, B.; De Rooze, N.; Schacht, E.H.; Cornelissen, M.; Berghmans, H. Structural and Rheological Properties of Methacrylamide Modified Gelatin Hydrogels. Biomacromolecul","[68, 1421, 1120, 1465]",reference_item,0.85,"[""reference content label: 155. Van Den Bulcke, A.I.; Bogdanov, B.; De Rooze, N.; Schac""]",reference_item,0.85,reference_zone,reference_like,reference_numeric_dot,True,True
32,26,reference_content,"156. Yue, K.; Trujillo-de Santiago, G.; Alvarez, M.M.; Tamayol, A.; Annabi, N.; Khademhosseini, A. Synthesis, Properties, and Biomedical Applications of Gelatin Methacryloyl (GelMA) Hydrogels. Biomate","[67, 1467, 1123, 1512]",reference_item,0.85,"[""reference content label: 156. Yue, K.; Trujillo-de Santiago, G.; Alvarez, M.M.; Tamay""]",reference_item,0.85,reference_zone,reference_like,reference_numeric_dot,True,True
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33,2,reference_content,"157. Young, A.T.; White, O.C.; Daniele, M.A. Rheological Properties of Coordinated Physical Gelation and Chemical Crosslinking in Gelatin Methacryloyl (GelMA) Hydrogels. Macromol. Biosci. 2020, 20, e2","[66, 177, 1121, 223]",reference_item,0.85,"[""reference content label: 157. Young, A.T.; White, O.C.; Daniele, M.A. Rheological Pro""]",reference_item,0.85,reference_zone,reference_like,reference_numeric_dot,True,True
33,3,reference_content,"158. Miri, A.K.; Hosseinabadi, H.G.; Cecen, B.; Hassan, S.; Zhang, Y.S. Permeability Mapping of Gelatin Methacryloyl Hydrogels. Acta Biomater. 2018, 77, 38–47. [CrossRef] [PubMed]","[68, 224, 1122, 267]",reference_item,0.85,"[""reference content label: 158. Miri, A.K.; Hosseinabadi, H.G.; Cecen, B.; Hassan, S.; ""]",reference_item,0.85,reference_zone,reference_like,reference_numeric_dot,True,True
33,4,reference_content,"159. Paul, S.; Schrobback, K.; Tran, P.A.; Meinert, C.; Davern, J.W.; Weekes, A.; Klein, T.J. Photo-cross-linkable, Injectable, and Highly Adhesive GelMA-φlvcol Chitosan Hydrogels for Cartilage Repair","[68, 270, 1122, 314]",reference_item,0.85,"[""reference content label: 159. Paul, S.; Schrobback, K.; Tran, P.A.; Meinert, C.; Dave""]",reference_item,0.85,reference_zone,reference_like,reference_numeric_dot,True,True
33,5,reference_content,"160. Liu, G.; Guo, Q.; Liu, C.; Bai, J.; Wang, H.; Li, J.; Liu, D.; Yu, Q.; Shi, J.; Liu, C.; et al. Cytomodulin-10 Modified GelMA Hydrogel with Kartogenin for in-Situ Osteochondral Regeneration. Acta","[67, 316, 1122, 360]",reference_item,0.85,"[""reference content label: 160. Liu, G.; Guo, Q.; Liu, C.; Bai, J.; Wang, H.; Li, J.; L""]",reference_item,0.85,reference_zone,reference_like,reference_numeric_dot,True,True
33,6,reference_content,"161. Sun, T.; Feng, Z.; He, W.; Li, C.; Han, S.; Li, Z.; Guo, R. Novel 3D-printing Bilayer GelMA-based Hydrogel Containing BP, Beta-TCP and Exosomes for Cartilage-bone Integrated Repair. Biofabricatio","[68, 362, 1122, 406]",reference_item,0.85,"[""reference content label: 161. Sun, T.; Feng, Z.; He, W.; Li, C.; Han, S.; Li, Z.; Guo""]",reference_item,0.85,reference_zone,reference_like,reference_numeric_dot,True,True
33,7,reference_content,"162. Yin, P.; Su, W.; Li, T.; Wang, L.; Pan, J.; Wu, X.; Shao, Y.; Chen, H.; Lin, L.; Yang, Y.; et al. A Modular Hydrogel Bioink Containing Microsphere-Embedded Chondrocytes for 3D-Printed Multiscale ","[68, 407, 1123, 473]",reference_item,0.85,"[""reference content label: 162. Yin, P.; Su, W.; Li, T.; Wang, L.; Pan, J.; Wu, X.; Sha""]",reference_item,0.85,reference_zone,reference_like,reference_numeric_dot,True,True
33,8,reference_content,"163. Gandini, A.; Lacerda, T.M. Monomers and Macromolecular Materials from Renewable Resources: State of the Art and Perspectives. Molecules 2021, 27, 159. [CrossRef] [PubMed]","[68, 477, 1123, 520]",reference_item,0.85,"[""reference content label: 163. Gandini, A.; Lacerda, T.M. Monomers and Macromolecular ""]",reference_item,0.85,reference_zone,reference_like,reference_numeric_dot,True,True
33,9,reference_content,"164. Li, G.; Zhao, M.; Xu, F.; Yang, B.; Li, X.; Meng, X.; Teng, L.; Sun, F.; Li, Y. Synthesis and Biological Application of Polylactic Acid. Molecules 2020, 25, 5023. [CrossRef] [PubMed]","[69, 522, 1123, 566]",reference_item,0.85,"[""reference content label: 164. Li, G.; Zhao, M.; Xu, F.; Yang, B.; Li, X.; Meng, X.; T""]",reference_item,0.85,reference_zone,reference_like,reference_numeric_dot,True,True
33,10,reference_content,"165. Farah, S.; Anderson, D.G.; Langer, R. Physical and Mechanical Properties of PLA, and Their Functions in Widespread Applications—A Comprehensive Review. Adv. Drug Deliv. Rev. 2016, 107, 367–392. [","[69, 569, 1120, 613]",reference_item,0.85,"[""reference content label: 165. Farah, S.; Anderson, D.G.; Langer, R. Physical and Mech""]",reference_item,0.85,reference_zone,reference_like,reference_numeric_dot,True,True
33,11,reference_content,"166. Oh, J.K. Polylactide (PLA)-Based Amphiphilic Block Copolymers: Synthesis, Self-Assembly, and Biomedical Applications. Soft Matter. 2011, 7, 5096. [CrossRef]","[68, 615, 1123, 657]",reference_item,0.85,"[""reference content label: 166. Oh, J.K. Polylactide (PLA)-Based Amphiphilic Block Copo""]",reference_item,0.85,reference_zone,reference_like,reference_numeric_dot,True,True
33,12,reference_content,"167. Zeng, Z. Recent Advances in PEG-PLA Block Copolymer Nanoparticles. Int. J. Nanomed. 2010, 5, 1057–1065. [CrossRef] [PubMed]","[67, 660, 1124, 684]",reference_item,0.85,"[""reference content label: 167. Zeng, Z. Recent Advances in PEG-PLA Block Copolymer Nan""]",reference_item,0.85,reference_zone,reference_like,reference_numeric_dot,True,True
33,13,reference_content,"168. Tamai, N.; Myoui, A.; Hirao, M.; Kaito, T.; Ochi, T.; Tanaka, J.; Takaoka, K.; Yoshikawa, H. A New Biotechnology for Articular Cartilage Repair: Subchondral Implantation of a Composite of Interco","[67, 683, 1123, 753]",reference_item,0.85,"[""reference content label: 168. Tamai, N.; Myoui, A.; Hirao, M.; Kaito, T.; Ochi, T.; T""]",reference_item,0.85,reference_zone,reference_like,reference_numeric_dot,True,True
33,14,reference_content,"169. Rahmani, F.; Atabaki, R.; Behrouzi, S.; Mohamadpour, F.; Kamali, H. The Recent Advancement in the PLGA-Based Thermo-Sensitive Hydrogel for Smart Drug Delivery. Int. J. Pharm. 2023, 631, 122484. [","[68, 753, 1124, 796]",reference_item,0.85,"[""reference content label: 169. Rahmani, F.; Atabaki, R.; Behrouzi, S.; Mohamadpour, F.""]",reference_item,0.85,reference_zone,reference_like,reference_numeric_dot,True,True
33,15,reference_content,"170. Li, S.; Niu, D.; Shi, T.; Yun, W.; Yan, S.; Xu, G.; Yin, J. Injectable, in Situ Self-Cross-Linking, Self-Healing Poly(l-Glutamic Acid)/Polyethylene Glycol Hydrogels for Cartilage Tissue Engineeri","[67, 799, 1123, 843]",reference_item,0.85,"[""reference content label: 170. Li, S.; Niu, D.; Shi, T.; Yun, W.; Yan, S.; Xu, G.; Yin""]",reference_item,0.85,reference_zone,reference_like,reference_numeric_dot,True,True
33,16,reference_content,"171. Xu, S.; Zhao, S.; Jian, Y.; Xu, Y.; Liu, W.; Shao, X.; Fan, J.; Wang, Y. Effect of Xanthohumol-Loaded Anti-Inflammatory Scaffolds on Cartilage Regeneration in Goats. Chin. J. Reparative Reconstr.","[68, 846, 1123, 889]",reference_item,0.85,"[""reference content label: 171. Xu, S.; Zhao, S.; Jian, Y.; Xu, Y.; Liu, W.; Shao, X.; ""]",reference_item,0.85,reference_zone,reference_like,reference_numeric_dot,True,True
33,17,reference_content,"172. Hedayati, H.R.; Khorasani, M.; Ahmadi, M.; Ballard, N. Preparation of Well-Defined Poly(Vinyl Alcohol) by Hydrolysis of Poly(Vinyl Acetate) Synthesized by Raft Suspension Polymerization. Polymer ","[68, 891, 1123, 935]",reference_item,0.85,"[""reference content label: 172. Hedayati, H.R.; Khorasani, M.; Ahmadi, M.; Ballard, N. ""]",reference_item,0.85,reference_zone,reference_like,reference_numeric_dot,True,True
33,18,reference_content,"173. Chua, C.K.; Leong, K.F.; Tan, K.H.; Wiria, F.E.; Cheah, C.M. Development of Tissue Scaffolds Using Selective Laser Sintering of Polyvinyl Alcohol/Hydroxyapatite Biocomposite for Craniofacial and ","[67, 937, 1123, 1003]",reference_item,0.85,"[""reference content label: 173. Chua, C.K.; Leong, K.F.; Tan, K.H.; Wiria, F.E.; Cheah,""]",reference_item,0.85,reference_zone,reference_like,reference_numeric_dot,True,True
33,19,reference_content,"174. Chen, Y.; Song, J.; Wang, S.; Liu, W. PVA-based Hydrogels: Promising Candidates for Articular Cartilage Repair. Macromol. Biosci. 2021, 21, e2100147. [CrossRef]","[66, 1006, 1122, 1050]",reference_item,0.85,"[""reference content label: 174. Chen, Y.; Song, J.; Wang, S.; Liu, W. PVA-based Hydroge""]",reference_item,0.85,reference_zone,reference_like,reference_numeric_dot,True,True
33,20,reference_content,"175. Teodorescu, M.; Bercea, M.; Morariu, S. Biomaterials of PVA and PVP in Medical and Pharmaceutical Applications: Perspectives and Challenges. Biotechnol. Adv. 2019, 37, 109–131. [CrossRef] [PubMed","[67, 1052, 1122, 1096]",reference_item,0.85,"[""reference content label: 175. Teodorescu, M.; Bercea, M.; Morariu, S. Biomaterials of""]",reference_item,0.85,reference_zone,reference_like,reference_numeric_dot,True,True
33,21,reference_content,"176. Zhu, C.; Huang, C.; Zhang, W.; Ding, X.; Yang, Y. Biodegradable-Glass-Fiber Reinforced Hydrogel Composite with Enhanced Mechanical Performance and Cell Proliferation for Potential Cartilage Repai","[68, 1098, 1121, 1141]",reference_item,0.85,"[""reference content label: 176. Zhu, C.; Huang, C.; Zhang, W.; Ding, X.; Yang, Y. Biode""]",reference_item,0.85,reference_zone,reference_like,reference_numeric_dot,True,True
33,22,reference_content,"177. Yao, H.; Kang, J.; Li, W.; Liu, J.; Xie, R.; Wang, Y.; Liu, S.; Wang, D.-A.; Ren, L. Novel $ \beta $-TCP/PVA Bilayered Hydrogels with Considerable Physical and Bio-Functional Properties for Oste","[68, 1144, 1122, 1210]",reference_item,0.85,"[""reference content label: 177. Yao, H.; Kang, J.; Li, W.; Liu, J.; Xie, R.; Wang, Y.; ""]",reference_item,0.85,reference_zone,reference_like,reference_numeric_dot,True,True
33,23,reference_content,"178. Bolandi, B.; Imani, R.; Bonakdar, S.; Fakhrzadeh, H. Chondrogenic Stimulation in Mesenchymal Stem Cells Using Scaffold-based Sustained Release of Platelet-rich Plasma. J. Appl. Polym. Sci. 2021, ","[68, 1213, 1120, 1258]",reference_item,0.85,"[""reference content label: 178. Bolandi, B.; Imani, R.; Bonakdar, S.; Fakhrzadeh, H. Ch""]",reference_item,0.85,reference_zone,reference_like,reference_numeric_dot,True,True
33,24,reference_content,"179. Mallakpour, S.; Tabesh, F.; Hussain, C.M. A New Trend of Using Poly (Vinyl Alcohol) in 3D and 4D Printing Technologies: Process and Applications. Adv. Colloid Interface Sci. 2022, 301, 102605. [C","[70, 1260, 1122, 1304]",reference_item,0.85,"[""reference content label: 179. Mallakpour, S.; Tabesh, F.; Hussain, C.M. A New Trend o""]",reference_item,0.85,reference_zone,reference_like,reference_numeric_dot,True,True
33,25,reference_content,"180. Kumar, A.; Han, S.S. PVA-Based Hydrogels for Tissue Engineering: A Review. Int. J. Polym. Mater. Polym. Biomater. 2016, 66, 159–182. [CrossRef]","[73, 1306, 1125, 1349]",reference_item,0.85,"[""reference content label: 180. Kumar, A.; Han, S.S. PVA-Based Hydrogels for Tissue Eng""]",reference_item,0.85,reference_zone,reference_like,reference_numeric_dot,True,True
33,26,reference_content,"181. Sionkowska, A.; Gadomska, M.; Musiał, K.; Piątek, J. Hyaluronic Acid as a Component of Natural Polymer Blends for Biomedical Applications: A Review. Molecules 2020, 25, 4035. [CrossRef] [PubMed]","[70, 1352, 1122, 1396]",reference_item,0.85,"[""reference content label: 181. Sionkowska, A.; Gadomska, M.; Musia\u0142, K.; Pi\u0105tek, J. Hy""]",reference_item,0.85,reference_zone,reference_like,reference_numeric_dot,True,True
33,27,reference_content,"182. Tamer, T.M. Hyaluronan and Synovial Joint: Function, Distribution and Healing. Interdiscip. Toxicol. 2013, 6, 111–125. [CrossRef] [PubMed]","[68, 1398, 1123, 1441]",reference_item,0.85,"[""reference content label: 182. Tamer, T.M. Hyaluronan and Synovial Joint: Function, Di""]",reference_item,0.85,reference_zone,reference_like,reference_numeric_dot,True,True
33,28,reference_content,"183. Quintana, L.; zur Nieden, N.I.; Semino, C.E. Morphogenetic and Regulatory Mechanisms during Developmental Chondrogenesis: New Paradigms for Cartilage Tissue Engineering. Tissue Eng. Part B Rev. 2","[67, 1443, 1123, 1488]",reference_item,0.85,"[""reference content label: 183. Quintana, L.; zur Nieden, N.I.; Semino, C.E. Morphogene""]",reference_item,0.85,reference_zone,reference_like,reference_numeric_dot,True,True
33,29,reference_content,"184. Ito, T.; Williams, J.D.; Fraser, D.J.; Phillips, A.O. Hyaluronan Regulates Transforming Growth Factor-B1 Receptor Compartmentalization. J. Biol. Chem. 2004, 279, 25326–25332. [CrossRef] [PubMed]","[67, 1490, 1124, 1534]",reference_item,0.85,"[""reference content label: 184. Ito, T.; Williams, J.D.; Fraser, D.J.; Phillips, A.O. H""]",reference_item,0.85,reference_zone,reference_like,reference_numeric_dot,True,True
34,0,header,"Gels 2024, 10, 430","[68, 99, 195, 118]",noise,0.9,"[""header label""]",noise,0.9,,unknown_like,short_fragment,False,False
34,1,number,34 of 35,"[1061, 103, 1122, 122]",noise,0.9,"[""page number label""]",noise,0.9,reference_zone,reference_like,reference_numeric_dot,False,False
34,2,reference_content,"185. Peterson, R.S.; Andhare, R.A.; Rousche, K.T.; Knudson, W.; Wang, W.; Grossfield, J.B.; Thomas, R.O.; Hollingsworth, R.E.; Knudson, C.B. CD44 Modulates SMAD1 Activation in the BMP-7 Signaling Path","[67, 177, 1122, 244]",reference_item,0.85,"[""reference content label: 185. Peterson, R.S.; Andhare, R.A.; Rousche, K.T.; Knudson, ""]",reference_item,0.85,reference_zone,reference_like,reference_numeric_dot,True,True
34,3,reference_content,"186. Wang, M.; Deng, Z.; Guo, Y.; Xu, P. Designing Functional Hyaluronic Acid-Based Hydrogels for Cartilage Tissue Engineering. Mater. Today Bio 2022, 17, 100495. [CrossRef] [PubMed]","[67, 247, 1123, 290]",reference_item,0.85,"[""reference content label: 186. Wang, M.; Deng, Z.; Guo, Y.; Xu, P. Designing Functiona""]",reference_item,0.85,reference_zone,reference_like,reference_numeric_dot,True,True
34,4,reference_content,"187. Masters, K.S.; Shah, D.N.; Leinwand, L.A.; Anseth, K.S. Crosslinked Hyaluronan Scaffolds as a Biologically Active Carrier for Valvular Interstitial Cells. Biomaterials 2005, 26, 2517–2525. [Cross","[67, 293, 1123, 336]",reference_item,0.85,"[""reference content label: 187. Masters, K.S.; Shah, D.N.; Leinwand, L.A.; Anseth, K.S.""]",reference_item,0.85,reference_zone,reference_like,reference_numeric_dot,True,True
34,5,reference_content,"188. Bencherif, S.A.; Srinivasan, A.; Horkay, F.; Hollinger, J.O.; Matyjaszewski, K.; Washburn, N.R. Influence of the Degree of Methacrylation on Hyaluronic Acid Hydrogels Properties. Biomaterials 200","[68, 339, 1123, 383]",reference_item,0.85,"[""reference content label: 188. Bencherif, S.A.; Srinivasan, A.; Horkay, F.; Hollinger,""]",reference_item,0.85,reference_zone,reference_like,reference_numeric_dot,True,True
34,6,reference_content,"189. Lam, T.; Dehne, T.; Krüger, J.P.; Hondke, S.; Endres, M.; Thomas, A.; Lauster, R.; Sittinger, M.; Kloke, L. Photopolymerizable Gelatin and Hyaluronic Acid for Stereolithographic 3D Bioprinting of","[68, 385, 1124, 450]",reference_item,0.85,"[""reference content label: 189. Lam, T.; Dehne, T.; Kr\u00fcger, J.P.; Hondke, S.; Endres, M""]",reference_item,0.85,reference_zone,reference_like,reference_numeric_dot,True,True
34,7,reference_content,"190. Martyniak, K.; Lokshina, A.; Cruz, M.A.; Karimzadeh, M.; Kean, T. Biomaterial Composition and Stiffness as Decisive Properties of 3D Bioprinted Constructs for Type II Collagen Stimulation. Acta B","[68, 453, 1123, 497]",reference_item,0.85,"[""reference content label: 190. Martyniak, K.; Lokshina, A.; Cruz, M.A.; Karimzadeh, M.""]",reference_item,0.85,reference_zone,reference_like,reference_numeric_dot,True,True
34,8,reference_content,"191. Kesti, M.; Müller, M.; Becher, J.; Schnabelrauch, M.; D'Este, M.; Eglin, D.; Zenobi-Wong, M. A Versatile Bioink for Three-dimensional Printing of Cellular Scaffolds Based on Thermally and Photo-t","[68, 499, 1123, 565]",reference_item,0.85,"[""reference content label: 191. Kesti, M.; M\u00fcller, M.; Becher, J.; Schnabelrauch, M.; D""]",reference_item,0.85,reference_zone,reference_like,reference_numeric_dot,True,True
34,9,reference_content,"192. Poldervaart, M.T.; Goversen, B.; de Ruijter, M.; Abbadessa, A.; Melchels, F.P.; Öner, F.C.; Dhert, W.J.; Vermonden, T.; Alblas, J. 3D Bioprinting of Methacrylated Hyaluronic Acid (MeHA) Hydrogel ","[68, 569, 1123, 635]",reference_item,0.85,"[""reference content label: 192. Poldervaart, M.T.; Goversen, B.; de Ruijter, M.; Abbade""]",reference_item,0.85,reference_zone,reference_like,reference_numeric_dot,True,True
34,10,reference_content,"193. Mallakpour, S.; Tukhani, M.; Hussain, C.M. Recent Advancements in 3D Bioprinting Technology of Carboxymethyl Cellulose-Based Hydrogels: Utilization in Tissue Engineering. Adv. Colloid Interface S","[68, 638, 1124, 683]",reference_item,0.85,"[""reference content label: 193. Mallakpour, S.; Tukhani, M.; Hussain, C.M. Recent Advan""]",reference_item,0.85,reference_zone,reference_like,reference_numeric_dot,True,True
34,11,reference_content,"194. Bonetti, L.; De Nardo, L.; Farè, S. Crosslinking Strategies in Modulating Methylcellulose Hydrogel Properties. Soft Matter. 2023, 19, 7869–7884. [CrossRef] [PubMed]","[67, 684, 1124, 727]",reference_item,0.85,"[""reference content label: 194. Bonetti, L.; De Nardo, L.; Far\u00e8, S. Crosslinking Strate""]",reference_item,0.85,reference_zone,reference_like,reference_numeric_dot,True,True
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34,13,reference_content,"196. Haque, A.; Morris, E.R. Thermogelation of Methylcellulose. Part I: Molecular Structures and Processes. Carbohydr. Polym. 1993, 22, 161–173. [CrossRef]","[68, 776, 1124, 818]",reference_item,0.85,"[""reference content label: 196. Haque, A.; Morris, E.R. Thermogelation of Methylcellulo""]",reference_item,0.85,reference_zone,reference_like,reference_numeric_dot,True,True
34,14,reference_content,"197. Roushangar Zineh, B.; Shabgard, M.R.; Roshangar, L. Mechanical and Biological Performance of Printed Alginate/Methylcellulose/Halloysite nanotube/Polyvinylidene Fluoride Bio-Scaffolds. Mater. Sci","[68, 821, 1123, 889]",reference_item,0.85,"[""reference content label: 197. Roushangar Zineh, B.; Shabgard, M.R.; Roshangar, L. Mec""]",reference_item,0.85,reference_zone,reference_like,reference_numeric_dot,True,True
34,15,reference_content,"198. Hu, M.; Yang, J.; Xu, J. Structural and Biological Investigation of Chitosan/Hyaluronic Acid with Silanized-Hydroxypropyl Methylcellulose as an Injectable Reinforced Interpenetrating Network Hydr","[68, 891, 1123, 958]",reference_item,0.85,"[""reference content label: 198. Hu, M.; Yang, J.; Xu, J. Structural and Biological Inve""]",reference_item,0.85,reference_zone,reference_like,reference_numeric_dot,True,True
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34,18,reference_content,"201. Hodder, E.; Duin, S.; Kilian, D.; Ahlfeld, T.; Seidel, J.; Nachtigall, C.; Bush, P.; Covill, D.; Gelinsky, M.; Lode, A. Investigating the Effect of Sterilisation Methods on the Physical Propertie","[66, 1098, 1122, 1166]",reference_item,0.85,"[""reference content label: 201. Hodder, E.; Duin, S.; Kilian, D.; Ahlfeld, T.; Seidel, ""]",reference_item,0.85,reference_zone,reference_like,heading_numbered,True,True
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34,23,reference_content,"206. Thomas, S.; Datta, J.; Haponiuk, J.; Reghunadhan, A. Polyurethane Polymers: Blends and Interpenetrating Polymer Networks; Elsevier Ltd.: Amsterdam, The Netherlands, 2017.","[67, 1352, 1122, 1395]",reference_item,0.85,"[""reference content label: 206. Thomas, S.; Datta, J.; Haponiuk, J.; Reghunadhan, A. Po""]",reference_item,0.85,reference_zone,reference_like,reference_numeric_dot,True,True
34,24,reference_content,"207. Cooper, S.L.; Guan, J.; Abraham, G.A. Advances in Polyurethane Biomaterials; Woodhead Publishing Ltd.: Cambridge, UK, 2016.","[67, 1397, 1111, 1419]",reference_item,0.85,"[""reference content label: 207. Cooper, S.L.; Guan, J.; Abraham, G.A. Advances in Polyu""]",reference_item,0.85,reference_zone,reference_like,reference_numeric_dot,True,True
34,25,reference_content,"208. Naureen, B.; Haseeb, A.S.M.A.; Basirun, W.J.; Muhamad, F. Recent Advances in Tissue Engineering Scaffold Based on Polyurethane and Modified Polyurethane. Mater. Sci. Eng. C Mater. Biol. Appl. 202","[67, 1422, 1119, 1464]",reference_item,0.85,"[""reference content label: 208. Naureen, B.; Haseeb, A.S.M.A.; Basirun, W.J.; Muhamad, ""]",reference_item,0.85,reference_zone,reference_like,reference_numeric_dot,True,True
34,26,reference_content,"209. Hung, K.C.; Tseng, C.S.; Dai, L.G.; Hsu, S. Water-Based Polyurethane 3D Printed Scaffolds with Controlled Release Function for Customized Cartilage Tissue Engineering. Biomaterials 2016, 83, 156–","[66, 1467, 1123, 1511]",reference_item,0.85,"[""reference content label: 209. Hung, K.C.; Tseng, C.S.; Dai, L.G.; Hsu, S. Water-Based""]",reference_item,0.85,reference_zone,reference_like,reference_numeric_dot,True,True
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35,2,reference_content,"210. Shie, M.Y.; Chang, W.C.; Wei, L.J.; Huang, Y.H.; Chen, C.H.; Shih, C.T.; Chen, Y.W.; Shen, Y.F. 3D Printing of Cytocompatible Water-Based Light-Cured Polyurethane with Hyaluronic Acid for Cartila","[67, 178, 1121, 244]",reference_item,0.85,"[""reference content label: 210. Shie, M.Y.; Chang, W.C.; Wei, L.J.; Huang, Y.H.; Chen, ""]",reference_item,0.85,reference_zone,reference_like,reference_numeric_dot,True,True
35,3,reference_content,"211. Chen, Y.W.; Shie, M.Y.; Chang, W.C.; Shen, Y.F. Approximate Optimization Study of Light Curing Waterborne Polyurethane Materials for the Construction of 3D Printed Cytocompatible Cartilage Scaffo","[68, 247, 1119, 291]",reference_item,0.85,"[""reference content label: 211. Chen, Y.W.; Shie, M.Y.; Chang, W.C.; Shen, Y.F. Approxi""]",reference_item,0.85,reference_zone,reference_like,reference_numeric_dot,True,True
35,4,reference_content,"212. Grad, S.; Kupcsik, L.; Gorna, K.; Gogolewski, S.; Alini, M. The Use of Biodegradable Polyurethane Scaffolds for Cartilage Tissue Engineering: Potential and Limitations. Biomaterials 2003, 24, 516","[68, 293, 1120, 339]",reference_item,0.85,"[""reference content label: 212. Grad, S.; Kupcsik, L.; Gorna, K.; Gogolewski, S.; Alini""]",reference_item,0.85,reference_zone,reference_like,reference_numeric_dot,True,True
35,5,text,"Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI ","[66, 367, 1123, 438]",backmatter_body,0.6,"[""default body_paragraph for text label"", ""tail_nonref_hold_zone excluded from body flow""]",backmatter_body,0.6,tail_nonref_hold_zone,support_like,none,True,True
1 page block_id raw_label content_preview bbox role role_confidence evidence seed_role seed_confidence zone style_family marker_type render_default index_default
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3 1 1 header_image [1029, 110, 1120, 170] non_body_insert 0.2 ["unrecognized label 'header_image'"] unknown_structural 0.2 frontmatter_main_zone support_like empty False False
4 1 2 text Review [66, 207, 135, 232] non_body_insert 0.3 ["short text, uncertain role"] unknown_structural 0.3 frontmatter_main_zone support_like short_fragment False False
5 1 3 doc_title Hydrogel-Based 3D Bioprinting Technology for Articular Cartilage Regenerative Engineering [66, 232, 1009, 313] paper_title 0.8 ["page-1 zone title_zone: Hydrogel-Based 3D Bioprinting Technology for Articular Carti"] paper_title 0.8 frontmatter_main_zone support_like none True True
6 1 4 text Hongji Zhang $ ^{1,2,3} $, Zheyuan Zhou $ ^{1,2,3} $, Fengjie Zhang $ ^{1,2,3} $ and Chao Wan $ ^{1,2,3,*} $ [66, 332, 801, 362] authors 0.8 ["page-1 zone author_zone: Hongji Zhang $ ^{1,2,3} $, Zheyuan Zhou $ ^{1,2,3} $, Feng"] authors 0.8 frontmatter_main_zone support_like none True True
7 1 5 text Key Laboratory of Regenerative Medicine, Ministry of Education, School of Biomedical Sciences, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong SAR, China; zhan1754@link.cuhk.edu.hk [327, 403, 1124, 470] affiliation 0.8 ["page-1 zone affiliation_zone: Key Laboratory of Regenerative Medicine, Ministry of Educati"] affiliation 0.8 frontmatter_main_zone support_like none True True
8 1 6 text $ ^{2} $ Center for Neuromusculoskeletal Restorative Medicine, Hong Kong Science Park, Hong Kong SAR, China $ ^{3} $ Key Laboratory of Regenerative Medicine (Shenzhen Base), Ministry of Education, S [326, 470, 1114, 557] affiliation 0.8 ["page-1 zone affiliation_zone: $ ^{2} $ Center for Neuromusculoskeletal Restorative Medicin"] affiliation 0.8 frontmatter_main_zone support_like affiliation_marker True True
9 1 7 text check for updates [69, 962, 178, 999] non_body_insert 0.3 ["short text, uncertain role"] unknown_structural 0.3 frontmatter_main_zone support_like short_fragment False False
10 1 8 text Received: 13 May 2024 Revised: 9 June 2024 Accepted: 21 June 2024 Published: 28 June 2024 [67, 1199, 221, 1292] frontmatter_noise 0.8 ["page-1 zone journal_furniture_zone: Received: 13 May 2024 \nRevised: 9 June 2024 \nAccepted: 21 "] frontmatter_noise 0.8 body_zone body_like none False False
11 1 9 text Academic Editor: Filippo Rossi Correspondence: cwan@cuhk.edu.hk [68, 1161, 268, 1182] frontmatter_noise 0.8 ["page-1 zone journal_furniture_zone: Academic Editor: Filippo Rossi\nCorrespondence: cwan@cuhk.edu"] frontmatter_noise 0.8 body_zone body_like none False False
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13 1 11 text Citation: Zhang, H.; Zhou, Z.; Zhang, F.; Wan, C. Hydrogel-Based 3D Bioprinting Technology for Articular Cartilage Regenerative Engineering. Gels 2024, 10, 430. https://doi.org/10.3390/gels10070430 [66, 1006, 306, 1147] frontmatter_noise 0.8 ["page-1 zone journal_furniture_zone: Citation: Zhang, H.; Zhou, Z.; Zhang, F.; Wan, C. Hydrogel-B"] frontmatter_noise 0.8 frontmatter_main_zone support_like none False False
14 1 12 image [69, 1313, 185, 1353] media_asset 0.85 ["media label: image"] media_asset 0.85 body_zone unknown_like empty True True
15 1 13 abstract Abstract: Articular cartilage is an avascular tissue with very limited capacity of self-regeneration. Trauma or injury-related defects, inflammation, or aging in articular cartilage can induce progres [327, 604, 1123, 995] abstract_body 0.85 ["abstract label from Paddle OCR"] abstract_body 0.85 frontmatter_main_zone support_like none True True
16 1 14 text Copyright: © 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) licen [66, 1363, 309, 1551] frontmatter_noise 0.8 ["page-1 zone journal_furniture_zone: Copyright: \u00a9 2024 by the authors.\nLicensee MDPI, Basel, Swit"] frontmatter_noise 0.8 body_zone support_like none False False
17 1 15 text [326, 1018, 1122, 1044] unknown_structural 0.3 ["short text, uncertain role"] unknown_structural 0.3 frontmatter_main_zone support_like empty False True
18 1 16 paragraph_title 1. Introduction [329, 1117, 472, 1140] section_heading 0.85 ["paragraph_title label with numbering: 1. Introduction"] section_heading 0.85 body_zone heading_like heading_numbered True True
19 1 17 text Osteoarthritis (OA) is one of the most common degenerative joint diseases with a high disability rate, whose incidence increases with an aging population and increasing obesity rates. OA is becoming i [324, 1148, 1124, 1298] body_paragraph 0.6 ["default body_paragraph for text label"] body_paragraph 0.6 body_zone body_like none True True
20 1 18 text Natural cartilage tissue is avascular and nerve-free, which limits its ability to repair itself. It has been reported that articular cartilage defects over 2 cm² in size in the human knee are hardly s [325, 1299, 1127, 1552] body_paragraph 0.6 ["default body_paragraph for text label"] body_paragraph 0.6 body_zone body_like none True True
21 1 19 footer Gels 2024, 10, 430. https://doi.org/10.3390/gels10070430 [67, 1625, 476, 1647] noise 0.9 ["footer label"] noise 0.9 body_zone body_like none False False
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25 2 2 text in cartilage repair. Using the traditional engineering approaches, the bio-mimic structure of articular cartilage is difficult to be rebuilt due to the fact that natural cartilage tissue has a complic [325, 176, 1125, 402] body_paragraph 0.6 ["default body_paragraph for text label"] body_paragraph 0.6 body_zone body_like none True True
26 2 3 text Three-dimensional bioprinting relies on two main factors, including (1) the design of bioink as precursors (biomaterials, cells, growth factors and drugs); (2) the use of the 3D bioprinting method to [324, 402, 1126, 731] body_paragraph 0.6 ["default body_paragraph for text label"] body_paragraph 0.6 body_zone body_like none True True
27 2 4 image [336, 750, 1102, 1253] media_asset 0.85 ["media label: image"] media_asset 0.85 body_zone unknown_like empty True True
28 2 5 figure_title Figure 1. Schematic diagram of 3D bioprinted hydrogel for articular cartilage tissue engineering. Chondrocytes, mesenchymal stem cells (MSCs) or chondroprogenitor cells are isolated, cultured and expa [326, 1268, 1125, 1448] figure_caption 0.92 ["figure_title label: Figure 1. Schematic diagram of 3D bioprinted hydrogel for ar"] figure_caption 0.92 display_zone legend_like figure_number True True
29 2 6 text In this review, we mainly summarized the current progress of different types of hydrogel biomaterials, including natural and synthetic hydrogels used for articular cartilage [326, 1466, 1124, 1519] body_paragraph 0.6 ["default body_paragraph for text label"] body_paragraph 0.6 body_zone body_like none True True
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32 3 2 text repair or regeneration. The challenges and improvement directions of these hydrogels in cartilage tissue engineering applications are also stated. [326, 176, 1122, 228] body_paragraph 0.6 ["default body_paragraph for text label"] body_paragraph 0.6 body_zone body_like none True True
33 3 3 paragraph_title 2. Traditional Approaches for Articular Cartilage Repair or Regeneration [327, 247, 995, 271] section_heading 0.85 ["paragraph_title label with numbering: 2. Traditional Approaches for Articular Cartilage Repair or "] section_heading 0.85 body_zone reference_like reference_numeric_dot True True
34 3 4 text Cartilage is a resilient, avascular and aneural connective tissue, consisting of chondrocytes in an ECM that is rich in collagen type II and proteoglycan. Some studies indicate that the average thickn [323, 276, 1126, 783] body_paragraph 0.6 ["default body_paragraph for text label"] body_paragraph 0.6 body_zone body_like none True True
35 3 5 image [331, 817, 1006, 1076] media_asset 0.85 ["media label: image"] media_asset 0.85 body_zone unknown_like empty True True
36 3 6 figure_title Figure 2. Schematic representation of articular cartilage structures in the knee joint. The structure of articular cartilage is organized into four distinct zones: the superficial, transitional, radia [325, 1108, 1124, 1209] figure_caption 0.92 ["figure_title label: Figure 2. Schematic representation of articular cartilage st"] figure_caption 0.92 display_zone legend_like figure_number True True
37 3 7 text In the fetal period, infancy, or childhood, articular cartilage has its own regenerative activity. However, articular cartilage cannot be regenerated in the adults because mature chondrocytes do not p [324, 1228, 1124, 1502] body_paragraph 0.6 ["default body_paragraph for text label"] body_paragraph 0.6 body_zone body_like none True True
38 3 8 text It is well known that collagens in the ECM, such as collagen type II, IX, X, and XI determine the tensile strength of articular cartilage. The amount of water in articular [326, 1503, 1125, 1553] body_paragraph 0.6 ["default body_paragraph for text label"] body_paragraph 0.6 body_zone body_like none True True
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41 4 2 text cartilage and the number of chemical bonds present in collagen molecules decrease with age. As a result, the articular cartilage of the elderly is prone to weakened elasticity and lacks effective resi [324, 176, 1123, 301] body_paragraph 0.6 ["default body_paragraph for text label"] body_paragraph 0.6 body_zone body_like none True True
42 4 3 text At present, the conservative treatments for articular cartilage injury mainly include anti-inflammatory, analgesic, and drug therapy, in addition to weight-bearing and physical therapy. However, many [324, 301, 1125, 728] body_paragraph 0.6 ["default body_paragraph for text label"] body_paragraph 0.6 body_zone body_like none True True
43 4 4 text Therefore, in view of the problems mentioned above, it is necessary to develop and optimize better methods to repair articular cartilage lesions. More importantly, the re-generated cartilage tissue ha [325, 728, 1125, 831] body_paragraph 0.6 ["default body_paragraph for text label"] body_paragraph 0.6 body_zone body_like none True True
44 4 5 paragraph_title 3. Tissue Engineering for Treatment of Cartilage Damages [327, 848, 864, 873] section_heading 0.85 ["paragraph_title label with numbering: 3. Tissue Engineering for Treatment of Cartilage Damages"] section_heading 0.85 body_zone reference_like reference_numeric_dot True True
45 4 6 text Cartilage tissue engineering is an interdisciplinary field that applies the principles of engineering and life sciences to develop biological substitutes that restore, maintain, or improve the structu [324, 878, 1125, 1487] body_paragraph 0.6 ["default body_paragraph for text label"] body_paragraph 0.6 body_zone body_like none True True
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47 5 1 header 5 of 35 [1069, 103, 1121, 123] noise 0.9 ["header label"] noise 0.9 reference_like reference_numeric_dot False False
48 5 2 paragraph_title 4. Three-Dimensional Bioprinting Materials for Articular Cartilage Repair or Regeneration [326, 176, 1012, 223] section_heading 0.85 ["paragraph_title label with numbering: 4. Three-Dimensional Bioprinting Materials for Articular Car"] section_heading 0.85 body_zone reference_like reference_numeric_dot True True
49 5 3 text Up to now, 3D printing technology has been considered to be one of the most promising technologies for cartilage tissue engineering, replacing damaged or non-functional cartilage tissue with 3D-printe [324, 229, 1125, 580] body_paragraph 0.6 ["default body_paragraph for text label"] body_paragraph 0.6 body_zone body_like none True True
50 5 4 text Currently, a variety of technologies have been developed to fulfil the requirements of 3D bioprinting. Among them, the most commonly used methods are extrusion-based bioprinting, inkjet bioprinting, l [325, 580, 1123, 680] body_paragraph 0.6 ["default body_paragraph for text label"] body_paragraph 0.6 body_zone body_like none True True
51 5 5 text (1) Extrusion-based bioprinting methods typically use a pneumatic actuator or screw device to feed material from a cartridge through a nozzle or needle for deposition. These methods, compatible with a [325, 681, 1125, 1133] body_paragraph 0.78 ["default body_paragraph for text label", "late role resolution: non-body family 'reference_like' overrides body_paragraph", "style_family_authority=reference_marker", "context_source=block"] body_paragraph 0.6 body_zone reference_like reference_numeric_parenthesis True True
52 5 6 text (2) Inkjet-based bioprinting is a non-contact printing method derived from desktop inkjet printing, where individual droplets create patterns on a substrate. During bioprinting, an energy drive deform [325, 1132, 1126, 1460] body_paragraph 0.78 ["default body_paragraph for text label", "late role resolution: non-body family 'reference_like' overrides body_paragraph", "style_family_authority=reference_marker", "context_source=block"] body_paragraph 0.6 body_zone reference_like reference_numeric_parenthesis True True
53 6 0 header Gels 2024, 10, 430 [67, 99, 195, 119] noise 0.9 ["header label"] noise 0.9 body_zone body_like short_fragment False False
54 6 1 number 6 of 35 [1069, 103, 1121, 123] noise 0.9 ["page number label"] noise 0.9 reference_like reference_numeric_dot False False
55 6 2 image [336, 178, 1111, 941] media_asset 0.85 ["media label: image"] media_asset 0.85 body_zone unknown_like empty True True
56 6 3 figure_title Figure 3. Schematic diagram of four methods of 3D bioprinting for articular cartilage tissue engineering. (a) Inkjet printers extrude cells and bioinks in the form of droplets, driven by thermal energ [326, 969, 1125, 1148] figure_caption 0.92 ["figure_title label: Figure 3. Schematic diagram of four methods of 3D bioprintin"] figure_caption 0.92 display_zone legend_like figure_number True True
57 6 4 text (3) Laser-assisted bioprinting utilizes a laser as the energy source to deposit biomaterials onto a substrate. This technique typically comprises three components: a pulsed laser source, a ribbon coat [325, 1167, 1126, 1467] body_paragraph 0.78 ["default body_paragraph for text label", "late role resolution: non-body family 'reference_like' overrides body_paragraph", "style_family_authority=reference_marker", "context_source=block"] body_paragraph 0.6 body_zone reference_like reference_numeric_parenthesis True True
58 6 5 text (4) Stereolithography bioprinting selectively solidifies a cell-laden bioink using photopolymerization in a layer-by-layer process controlled by a moveable stage along the z-axis. A 2D pattern is proj [325, 1468, 1127, 1546] body_paragraph 0.78 ["default body_paragraph for text label", "late role resolution: non-body family 'reference_like' overrides body_paragraph", "style_family_authority=reference_marker", "context_source=block"] body_paragraph 0.6 body_zone reference_like reference_numeric_parenthesis True True
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61 7 2 text 3D structures without a printhead moving in the x-y directions. This results in a faster bioprinting process compared to other nozzle-based bioprinters [57]. Unlike traditional extrusion printing, ste [326, 176, 1124, 402] body_paragraph 0.6 ["default body_paragraph for text label", "promoted in tail spread from body_paragraph"] body_paragraph 0.6 body_zone body_like none True True
62 7 3 text Bioink is one of the major elements for 3D bioprinting. There are many biomaterials that can be used as bioinks. Among these biomaterials, hydrogels play the most important role in 3D bioprinting and [325, 402, 1124, 731] body_paragraph 0.6 ["default body_paragraph for text label", "promoted in tail spread from body_paragraph"] body_paragraph 0.6 body_zone body_like none True True
63 7 4 paragraph_title 4.1. Natural Hydrogels [328, 748, 525, 773] subsection_heading 0.85 ["paragraph_title label with numbering: 4.1. Natural Hydrogels"] subsection_heading 0.85 body_zone heading_like heading_numbered True True
64 7 5 text The natural hydrogel is mainly based on proteins and polysaccharides of the extracellular matrix (ECM). The protein-based hydrogels include gelatin, fibrin, elastin, and silk fibroin. The polysacchari [325, 779, 1126, 1059] body_paragraph 0.6 ["default body_paragraph for text label", "promoted in tail spread from body_paragraph"] body_paragraph 0.6 body_zone body_like none True True
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67 8 2 figure_title Table 1. Overview of some used natural hydrogels for cartilage tissue engineering. [325, 175, 992, 199] table_caption 0.9 ["table prefix matched: Table 1. Overview of some used natural hydrogels for cartila"] table_caption 0.9 display_zone table_caption_like table_number True True
68 8 3 table <table><tr><td>Name</td><td>Chemical</td><td>Advantages</td><td>Limitations</td><td>References</td></tr><tr><td>Alginate</td><td><img src="imgs/img_in_image_box_360_248_595_348.jpg" alt="Image"" /></t [69, 210, 1615, 735] media_asset 0.85 ["media label: table"] media_asset 0.85 body_zone body_like none True True
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70 9 1 number 9 of 35 [1069, 103, 1121, 123] noise 0.9 ["page number label"] noise 0.9 reference_like reference_numeric_dot False False
71 9 2 paragraph_title 4.1.1. Alginate [327, 176, 465, 200] subsection_heading 0.85 ["paragraph_title label with numbering: 4.1.1. Alginate"] subsection_heading 0.85 body_zone heading_like heading_numbered True True
72 9 3 text Alginate is a natural polysaccharide extracted from brown algae, which is highly hydrophilic and biocompatible. It is characterized by being non-cytotoxic, cost-effective, and eliciting a low immune r [324, 207, 1124, 483] body_paragraph 0.6 ["default body_paragraph for text label", "promoted in tail spread from body_paragraph"] body_paragraph 0.6 body_zone body_like none True True
73 9 4 text However, alginate hydrogels are easily degraded over time in vivo due to the loss of calcium ions and thus are also considered to be lacking the biological stability of scaffolds. In the past decade, [325, 483, 1125, 910] body_paragraph 0.6 ["default body_paragraph for text label", "promoted in tail spread from body_paragraph"] body_paragraph 0.6 body_zone body_like none True True
74 9 5 text Kosik-Kozioł et al. formulated an alginate/short submicron polylactide ink, which was able to increase Young's modulus of bioprinted scaffold and neocartilage ECM deposition [82]. Kilian et al. fabric [325, 910, 1125, 1387] body_paragraph 0.6 ["default body_paragraph for text label", "promoted in tail spread from body_paragraph"] body_paragraph 0.6 body_zone body_like none True True
75 9 6 text Yu X et al. formulated a double-layer hydrogel scaffold by using agarose as a base material, in which a sodium alginate (SA)/agarose layer was used for the repair of artificially produced subchondral [325, 1388, 1125, 1539] body_paragraph 0.78 ["default body_paragraph for text label", "late role resolution: non-body family 'reference_like' overrides body_paragraph", "style_family_authority=reference_marker", "context_source=block"] body_paragraph 0.6 body_zone reference_like citation_line True True
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78 10 2 text a preliminary calcified layer. This clearly showed that in situ fabrication of an anisotropic double-layer hydrogel as a bioscaffold could promote the functional repair of articular cartilage and subc [325, 176, 1123, 275] body_paragraph 0.6 ["default body_paragraph for text label", "promoted in tail spread from body_paragraph"] body_paragraph 0.6 body_zone body_like none True True
79 10 3 text Despite these advancements, there are several drawbacks associated with using alginate for cartilage tissue engineering. For example, due to the lack of adhesive ligands and the existence of negative [324, 278, 1125, 603] body_paragraph 0.6 ["default body_paragraph for text label", "promoted in tail spread from body_paragraph"] body_paragraph 0.6 body_zone body_like none True True
80 10 4 paragraph_title 4.1.2. HA [328, 622, 420, 645] subsection_heading 0.85 ["paragraph_title label with numbering: 4.1.2. HA"] subsection_heading 0.85 body_zone heading_like heading_numbered True True
81 10 5 text HA is a natural glycosaminoglycan characterized by repeating disaccharide units of glucuronic acid and N-acetylglucosamine [93]. As one of the major components in the ECM, HA significantly enhances ch [324, 653, 1125, 854] body_paragraph 0.6 ["default body_paragraph for text label", "promoted in tail spread from body_paragraph"] body_paragraph 0.6 body_zone body_like none True True
82 10 6 text In addition, aggrecan and other proteoglycans, which are the matrix components, can also act as receptors for HA and promotes chondrogenesis [96]. Due to its unique relationship with chondrocytes, HA [325, 855, 1124, 955] body_paragraph 0.6 ["default body_paragraph for text label", "promoted in tail spread from body_paragraph"] body_paragraph 0.6 body_zone body_like none True True
83 10 7 text Current research efforts primarily focus on the application of HA-based injectable hydrogels and the use as 3D printing bioinks. Injectable HA hydrogels, including aldehyde HA-based, thiolated HA-base [324, 954, 1127, 1535] body_paragraph 0.6 ["default body_paragraph for text label", "promoted in tail spread from body_paragraph"] body_paragraph 0.6 body_zone body_like none True True
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86 11 2 text ELP (ELP-HYD) and aldehyde-modified HA (HA-ALD). The hydrogels were fabricated with variable HA concentrations (1.5%, 3%, or 5%), and chondrocytes were encapsulated. The results showed that increasing [324, 176, 1124, 577] body_paragraph 0.6 ["default body_paragraph for text label", "promoted in tail spread from body_paragraph"] body_paragraph 0.6 body_zone body_like none True True
87 11 3 text HA is emerging as a promising biomaterial applied in the cartilage tissue engineering, given its biocompatibility, biodegradation, and the favorable biomimetic environment it creates, promoting cell a [324, 577, 1124, 756] body_paragraph 0.6 ["default body_paragraph for text label", "promoted in tail spread from body_paragraph"] body_paragraph 0.6 body_zone body_like none True True
88 11 4 paragraph_title 4.1.3. Collagen [328, 773, 468, 798] subsection_heading 0.85 ["paragraph_title label with numbering: 4.1.3. Collagen"] subsection_heading 0.85 body_zone heading_like heading_numbered True True
89 11 5 text Collagen exists mainly in the connective tissue of mammals and is an important component of the ECM. It is widely found in the bones, tendons, muscle sheaths, and in the membranes, ligaments, cartilag [325, 804, 1124, 1057] body_paragraph 0.6 ["default body_paragraph for text label", "promoted in tail spread from body_paragraph"] body_paragraph 0.6 body_zone body_like none True True
90 11 6 text In the field of cartilage tissue engineering, COL I and COL II are the most commonly used for cartilage regeneration. Yang et al. used COL I or agarose (AG) mixed with sodium alginate (SA) to serve as [326, 1055, 1125, 1534] body_paragraph 0.6 ["default body_paragraph for text label", "promoted in tail spread from body_paragraph"] body_paragraph 0.6 body_zone body_like none True True
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93 12 2 text Moreover, it played pivotal roles in promoting the chondrogenic differentiation of BMSCs and regenerating the cartilage defects in vivo [113]. However, directly applying collagen as a source for 3D bi [322, 175, 1127, 1055] body_paragraph 0.6 ["default body_paragraph for text label", "promoted in tail spread from body_paragraph"] body_paragraph 0.6 body_zone body_like none True True
94 12 3 text In cartilage tissue engineering, collagen-based cartilage scaffolds can enhance the expression of chondrogenic marker genes and promote cartilage regeneration, which effectively reduces the risk of im [324, 1055, 1126, 1334] body_paragraph 0.6 ["default body_paragraph for text label", "promoted in tail spread from body_paragraph"] body_paragraph 0.6 body_zone body_like none True True
95 12 4 paragraph_title 4.1.4. SF [328, 1350, 410, 1373] subsection_heading 0.85 ["paragraph_title label with numbering: 4.1.4. SF"] subsection_heading 0.85 body_zone heading_like heading_numbered True True
96 12 5 text SF is recognized as a natural biomaterial, spun by silkworms and spiders [123]. It is composed of 43% glycine, 30% alanine, and 12% serine [124]. The SF fiber consists of bundles of interlocking nanof [325, 1381, 1125, 1534] body_paragraph 0.6 ["default body_paragraph for text label", "promoted in tail spread from body_paragraph"] body_paragraph 0.6 body_zone body_like none True True
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99 13 2 text SF, SF needs to undergo processes such as degumming, washing, and drying $ [126] $. Similar to the biomaterials mentioned above, SF also exhibits various characterizations, such as favorable mechanic [324, 175, 1125, 351] body_paragraph 0.6 ["default body_paragraph for text label", "promoted in tail spread from body_paragraph"] body_paragraph 0.6 body_zone body_like none True True
100 13 3 text Due to its exceptional biocompatibility, SF serves as an ideal 3D matrix and bioscaffold for enhancing cell adhesion, proliferation, and differentiation in tissue engineering. Moreover, owing to its d [323, 351, 1128, 1536] body_paragraph 0.6 ["default body_paragraph for text label", "promoted in tail spread from body_paragraph"] body_paragraph 0.6 body_zone body_like none True True
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102 14 1 header 14 of 35 [1062, 103, 1121, 123] noise 0.9 ["header label"] noise 0.9 reference_like reference_numeric_dot False False
103 14 2 text high efficacy of SF-HPCMA in repairing rabbit articular cartilage defects, indicating its therapeutic potential for clinical applications $ [140] $. [325, 176, 1122, 226] body_paragraph 0.6 ["default body_paragraph for text label", "promoted in tail spread from body_paragraph"] body_paragraph 0.6 body_zone body_like none True True
104 14 3 text Despite the fact that SF possess great biocompatibility, low immunogenicity, and slow degradation, the SF materials and scaffolds formed from SF for tissue engineering sometimes show biological and me [326, 403, 1124, 604] body_paragraph 0.6 ["default body_paragraph for text label", "promoted in tail spread from body_paragraph"] body_paragraph 0.6 body_zone body_like none True True
105 14 4 text In addition, a decellularized extracellular matrix (dECM) was applied as a natural biomaterial similar to SF for tissue regeneration. Zhou et al. constructed dECM-SF hydrogel by a second crosslinking [325, 227, 1124, 401] body_paragraph 0.6 ["default body_paragraph for text label", "promoted in tail spread from body_paragraph"] body_paragraph 0.6 body_zone body_like none True True
106 14 5 paragraph_title 4.2. Synthetic Hydrogels [329, 622, 538, 647] subsection_heading 0.85 ["paragraph_title label with numbering: 4.2. Synthetic Hydrogels"] subsection_heading 0.85 body_zone heading_like heading_numbered True True
107 14 6 text Although natural hydrogels possess many advantages, such as low immunogenicity, great biocompatibility, a favorable biomimetic environment and cell growth, they also have drawbacks, including low mech [325, 652, 1125, 1060] body_paragraph 0.6 ["default body_paragraph for text label", "promoted in tail spread from body_paragraph"] body_paragraph 0.6 body_zone body_like none True True
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110 15 2 figure_title Table 2. Overview of some used synthetic hydrogels for cartilage tissue engineering. [326, 175, 1007, 199] table_caption 0.9 ["table prefix matched: Table 2. Overview of some used synthetic hydrogels for carti"] table_caption 0.9 display_zone table_caption_like table_number True True
111 15 3 table <table><tr><td>Name</td><td>Chemical</td><td>Advantages</td><td>Limitations</td><td>References</td></tr><tr><td>Polyethylene glycol (PEG)</td><td><img src="imgs/img_in_image_box_431_275_634_366.jpg" a [68, 206, 1615, 896] media_asset 0.85 ["media label: table"] media_asset 0.85 body_zone body_like none True True
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113 16 1 number 16 of 35 [1552, 103, 1617, 123] noise 0.9 ["page number label"] noise 0.9 reference_like reference_numeric_dot False False
114 16 2 figure_title Table 2. Cont. [326, 175, 447, 197] table_caption 0.9 ["table prefix matched: Table 2. Cont."] table_caption 0.9 display_zone table_caption_like table_number True True
115 16 3 table <table><tr><td>Name</td><td>Chemical</td><td>Advantages</td><td>Limitations</td><td>References</td></tr><tr><td>Hyaluronic acid methacrylate (HAMA)</td><td><img src="imgs/img_in_image_box_443_273_627_ [71, 209, 1612, 818] media_asset 0.85 ["media label: table"] media_asset 0.85 body_zone body_like none True True
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118 17 2 paragraph_title 4.2.1. PEG-Based Hydrogel [327, 175, 577, 201] subsection_heading 0.85 ["paragraph_title label with numbering: 4.2.1. PEG-Based Hydrogel"] subsection_heading 0.85 body_zone heading_like heading_numbered True True
119 17 3 text As an FDA-approved synthetic polymer, polyethylene glycol (PEG) assumes a vital role in the field of biomedical applications such as tissue engineering and drug delivery, particularly in cartilage reg [324, 207, 1125, 434] body_paragraph 0.6 ["default body_paragraph for text label", "promoted in tail spread from body_paragraph"] body_paragraph 0.6 body_zone body_like none True True
120 17 4 text In recent years, photopolymerization, utilizing light to convert liquid PEG macromer solutions into solid hydrogels under physiological conditions, has been developed and plays a unique and primary ro [324, 433, 1125, 911] body_paragraph 0.6 ["default body_paragraph for text label", "promoted in tail spread from body_paragraph"] body_paragraph 0.6 body_zone body_like none True True
121 17 5 text Although photopolymerization stands out as a fast and convenient method for creating PEG hydrogels, significant apprehensions emerge concerning cell exposure to light, particularly in the ultraviolet [324, 911, 1124, 1287] body_paragraph 0.6 ["default body_paragraph for text label", "promoted in tail spread from body_paragraph"] body_paragraph 0.6 body_zone body_like none True True
122 17 6 text In addition, PEG hydrogels have been extensively utilized as matrices to control drug delivery and as vehicles for cell delivery for tissue regeneration because of their optimal biodegradability, hydr [325, 1287, 1126, 1540] body_paragraph 0.6 ["default body_paragraph for text label", "promoted in tail spread from body_paragraph"] body_paragraph 0.6 body_zone body_like none True True
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125 18 2 text ing with cysteine–arginine–glycine–aspartic acid (CRGA, a cell adhesion peptide) via the Michael Addition Reaction. Due to the improved cell adhesion capability and bioactivity, CRGA-conjugated PEG fa [324, 176, 1125, 352] body_paragraph 0.6 ["default body_paragraph for text label", "promoted in tail spread from body_paragraph"] body_paragraph 0.6 body_zone body_like none True True
126 18 3 paragraph_title 4.2.2. GelMA-Based Hydrogel [327, 371, 602, 396] subsection_heading 0.85 ["paragraph_title label with numbering: 4.2.2. GelMA-Based Hydrogel"] subsection_heading 0.85 body_zone heading_like heading_numbered True True
127 18 4 text Gelatin, a protein found in the ECM and derived from denatured collagen, is a natural material with biocompatible and biodegradable properties. Although gelatin itself forms a gelatinous structure, it [325, 402, 1124, 629] body_paragraph 0.6 ["default body_paragraph for text label", "promoted in tail spread from body_paragraph"] body_paragraph 0.6 body_zone body_like none True True
128 18 5 text GelMA is produced by reacting gelatin with methacrylic anhydride (MA). Briefly, this reaction replaces numerous amino groups on gelatin's side chains with methacryloyl groups and forms a synthetic mat [324, 631, 1126, 1535] body_paragraph 0.6 ["default body_paragraph for text label", "promoted in tail spread from body_paragraph"] body_paragraph 0.6 body_zone body_like none True True
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131 19 2 text which had a relatively lower elastic modulus and was conducive to the differentiation of BMSCs into chondrocytes. In the lower scaffold, in addition to BP and hUMSCs exos, $ \beta $-tricalcium phosph [325, 176, 1126, 604] body_paragraph 0.6 ["default body_paragraph for text label", "promoted in tail spread from body_paragraph"] body_paragraph 0.6 body_zone body_like none True True
132 19 3 text In conclusion, GelMA plays a unique and irreplaceable role in biomedical applications because of its advantageous biocompatibility, bioactivity, and permeability (Figure 4). Although its mechanical pr [324, 603, 1125, 757] body_paragraph 0.6 ["default body_paragraph for text label", "promoted in tail spread from body_paragraph"] body_paragraph 0.6 body_zone body_like none True True
133 19 4 image [331, 781, 991, 1465] media_asset 0.85 ["media label: image"] media_asset 0.85 body_zone unknown_like empty True True
134 19 5 figure_title Figure 4. Schematic illustration of GelMA-based hydrogels. (A) The GelMA-glycol chitosan hydrogel exhibited excellent mechanical properties and an appropriate degradation period while maintaining [326, 1490, 1125, 1543] figure_caption 0.92 ["figure_title label: Figure 4. Schematic illustration of GelMA-based hydrogels. ("] figure_caption 0.92 display_zone legend_like figure_number True True
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137 20 2 text high adhesive capability. (B) The increased expression of chondrogenic markers in encapsulated BMSCs suggested that Cytomodulin-10-modified GelMA with Kartogenin holds significant potential for enhanc [326, 187, 1125, 391] body_paragraph 0.6 ["default body_paragraph for text label", "promoted in tail spread from body_paragraph"] body_paragraph 0.6 body_zone body_like none True True
138 20 3 paragraph_title 4.2.3. PLA- and PLA Copolymer-Based Hydrogel [327, 406, 772, 431] subsection_heading 0.85 ["paragraph_title label with numbering: 4.2.3. PLA- and PLA Copolymer-Based Hydrogel"] subsection_heading 0.85 body_zone heading_like heading_numbered True True
139 20 4 text Polylactic acid (PLA), a semi-crystalline polymer with a gradual crystallization process, distinguishes itself in the field of biomedical applications owing to its compatibility with host tissues, hyd [325, 437, 1125, 638] body_paragraph 0.6 ["default body_paragraph for text label", "promoted in tail spread from body_paragraph"] body_paragraph 0.6 body_zone body_like none True True
140 20 5 text The intermolecular forces governing PLA chains involve hydrogen bonding, hydrophobic, and ionic interactions. While the inherent hydrophobicity is advantageous for trapping drug-loaded nanoparticles, [324, 641, 1125, 1465] body_paragraph 0.6 ["default body_paragraph for text label", "promoted in tail spread from body_paragraph"] body_paragraph 0.6 body_zone body_like none True True
141 20 6 text In clinical applications, PLA stands out as one of the most prevalent biodegradable polymers. Despite the hydrophobic nature of these polymers and their lack of functional side groups for chemical cro [324, 1466, 1124, 1545] body_paragraph 0.6 ["default body_paragraph for text label", "promoted in tail spread from body_paragraph"] body_paragraph 0.6 body_zone body_like none True True
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144 21 2 text form of PLA-based hydrogels, proves invaluable. Through strategic integration with other polymers, PLA-based hydrogels continue to play essential roles in the field of cartilage tissue engineering. [326, 175, 1123, 253] body_paragraph 0.6 ["default body_paragraph for text label", "promoted in tail spread from body_paragraph"] body_paragraph 0.6 body_zone body_like none True True
145 21 3 paragraph_title 4.2.4. PVA-Based Hydrogel [328, 271, 577, 295] subsection_heading 0.85 ["paragraph_title label with numbering: 4.2.4. PVA-Based Hydrogel"] subsection_heading 0.85 body_zone heading_like heading_numbered True True
146 21 4 text Poly-vinyl alcohol (PVA) is created through the alkaline hydrolysis of poly-vinyl acetate, demonstrating notable properties, such as exceptional biocompatibility, suitable biodegradation rate, and non [324, 302, 1125, 504] body_paragraph 0.6 ["default body_paragraph for text label", "promoted in tail spread from body_paragraph"] body_paragraph 0.6 body_zone body_like none True True
147 21 5 text PVA-based hydrogels, with biomimetic mechanical properties resembling natural cartilage, form colloidal dispersions with 3D network structures through either physical or chemical crosslinking. These h [323, 503, 1125, 1260] body_paragraph 0.6 ["default body_paragraph for text label", "promoted in tail spread from body_paragraph"] body_paragraph 0.6 body_zone body_like none True True
148 21 6 text Looking ahead, the evolution of PVA hydrogels may progress towards a smart and patient-specific direction. Innovative preparation methods, such as 3D printing and in situ gelation have the potential t [324, 1256, 1125, 1511] body_paragraph 0.6 ["default body_paragraph for text label", "promoted in tail spread from body_paragraph"] body_paragraph 0.6 body_zone body_like none True True
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150 22 1 number 22 of 35 [1061, 103, 1122, 123] noise 0.9 ["page number label"] noise 0.9 reference_like reference_numeric_dot False False
151 22 2 paragraph_title 4.2.5. HAMA-Based Hydrogel [327, 175, 605, 201] subsection_heading 0.85 ["paragraph_title label with numbering: 4.2.5. HAMA-Based Hydrogel"] subsection_heading 0.85 body_zone heading_like heading_numbered True True
152 22 3 text HA is a type of GAG widely present in various tissues, especially in cartilage [181,182]. Although pure HA plays a crucial role in the cartilage matrix by inhibiting close mesenchymal cell–cell intera [324, 207, 1125, 507] body_paragraph 0.6 ["default body_paragraph for text label", "promoted in tail spread from body_paragraph"] body_paragraph 0.6 body_zone body_like none True True
153 22 4 text The creation of a hydrogel from HAMA involves the covalent crosslinking of the methacrylate polymers. Radical polymerization of the polymer-bound (meth) acrylate groups can be initiated either thermal [323, 507, 1126, 1465] body_paragraph 0.6 ["default body_paragraph for text label", "promoted in tail spread from body_paragraph"] body_paragraph 0.6 body_zone body_like none True True
154 22 5 text Boasting a swift photosensitive response, rapid gelation speed, and consistent hydrogel performance, the HAMA hydrogel emerges as a viable 3D printable biomaterial suitable for crafting customized sca [325, 1462, 1124, 1540] body_paragraph 0.6 ["default body_paragraph for text label", "promoted in tail spread from body_paragraph"] body_paragraph 0.6 body_zone body_like none True True
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157 23 2 text presence of HA in HAMA bestows it with significant potential to foster chondrogenesis, ultimately contributing to enhanced repair of cartilage by orchestrating the regulation of chondrogenic genes and [324, 176, 1125, 429] body_paragraph 0.6 ["default body_paragraph for text label", "promoted in tail spread from body_paragraph"] body_paragraph 0.6 body_zone body_like none True True
158 23 3 paragraph_title 4.2.6. Methylcellulose-Based Hydrogel [328, 447, 678, 472] subsection_heading 0.85 ["paragraph_title label with numbering: 4.2.6. Methylcellulose-Based Hydrogel"] subsection_heading 0.85 body_zone heading_like heading_numbered True True
159 23 4 text In recent years, cellulose (a natural polysaccharide) has garnered significant attention due to its notable properties, including excellent gel formation, non-toxicity, high biocompatibility, biodegra [324, 478, 1125, 957] body_paragraph 0.6 ["default body_paragraph for text label", "promoted in tail spread from body_paragraph"] body_paragraph 0.6 body_zone body_like none True True
160 23 5 text MC exhibits exceptional thermosensitive gelation characteristics, rheological attributes, non-toxicity, biocompatibility, and a structurally uncomplicated nature. It functions as an inherently functio [325, 956, 1125, 1534] body_paragraph 0.6 ["default body_paragraph for text label", "promoted in tail spread from body_paragraph"] body_paragraph 0.6 body_zone body_like none True True
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162 24 1 number 24 of 35 [1061, 103, 1121, 123] noise 0.9 ["page number label"] noise 0.9 reference_like reference_numeric_dot False False
163 24 2 text mechanical stimulation, the MC-based hydrogel serves as a suitable vehicle for MSC retention, fostering load-induced chondrogenesis while mitigating the hypertrophic effects of chondrocytes [200]. Fur [325, 176, 1125, 502] body_paragraph 0.6 ["default body_paragraph for text label", "promoted in tail spread from body_paragraph"] body_paragraph 0.6 body_zone body_like none True True
164 24 3 text In summary, it has been demonstrated that synthesizing MC-based hydrogels is relatively straightforward, and their mechano-rheological behavior is highly adjustable by manipulating the concentration o [325, 502, 1124, 754] body_paragraph 0.6 ["default body_paragraph for text label", "promoted in tail spread from body_paragraph"] body_paragraph 0.6 body_zone body_like none True True
165 24 4 paragraph_title 4.2.7. Polyurethane-Based Hydrogel [328, 773, 656, 798] subsection_heading 0.85 ["paragraph_title label with numbering: 4.2.7. Polyurethane-Based Hydrogel"] subsection_heading 0.85 body_zone heading_like heading_numbered True True
166 24 5 text Enhancing the field of regenerative medicine for cartilage repair relies on the utilization of qualified biomaterials capable of restoring or repairing damaged bodily tissue while withstanding signifi [326, 805, 1125, 1030] body_paragraph 0.6 ["default body_paragraph for text label", "promoted in tail spread from body_paragraph"] body_paragraph 0.6 body_zone body_like none True True
167 24 6 text Bayer and colleagues first synthesized PU in the 1930s [204]. Typically, PU is synthesized through a polyaddition reaction involving diisocyanates and diols. Under catalyst-free conditions, the alcoho [326, 1030, 1126, 1534] body_paragraph 0.6 ["default body_paragraph for text label", "promoted in tail spread from body_paragraph"] body_paragraph 0.6 body_zone body_like none True True
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170 25 2 text the cells seeded in the scaffolds successfully produced a cartilage-specific ECM, indicating the material's significant potential for cartilage regeneration [209]. Shie MY et al. developed water-based [326, 176, 1124, 376] body_paragraph 0.6 ["default body_paragraph for text label", "promoted in tail spread from body_paragraph"] body_paragraph 0.6 body_zone body_like none True True
171 25 3 text Further, Chen YW et al. demonstrated through experimental design that they explored the optimal parameters of a suitable biological scaffold modified with light-curing materials, which could facilitat [325, 377, 1124, 527] body_paragraph 0.6 ["default body_paragraph for text label", "promoted in tail spread from body_paragraph"] body_paragraph 0.6 body_zone body_like none True True
172 25 4 text Beyond 3D bioprinting applications, the direct biological interactions between chondrocytes and PU hydrogel have been already explored. Grad et al. seeded calve-derived chondrocytes in the PU scaffold [324, 527, 1125, 803] body_paragraph 0.6 ["default body_paragraph for text label", "promoted in tail spread from body_paragraph"] body_paragraph 0.6 body_zone body_like none True True
173 25 5 text As stated above, PU stands out among synthetic and natural biodegradable polymers for its exceptional mechanical and physical properties, which align with human tissues. However, the consideration of [325, 803, 1125, 1082] body_paragraph 0.6 ["default body_paragraph for text label", "promoted in tail spread from body_paragraph"] body_paragraph 0.6 body_zone body_like none True True
174 25 6 paragraph_title 5. Conclusions, Challenges and Prospects [328, 1100, 714, 1124] section_heading 0.85 ["paragraph_title label with numbering: 5. Conclusions, Challenges and Prospects"] section_heading 0.85 body_zone heading_like heading_numbered True True
175 25 7 text Three-dimensional bioprinting technology has been widely used in the study of articular cartilage repair or regeneration. Hydrogels, both naturally derived and chemically synthesized, can be optimized [326, 1131, 1124, 1408] body_paragraph 0.6 ["default body_paragraph for text label", "promoted in tail spread from body_paragraph"] body_paragraph 0.6 body_zone body_like none True True
176 25 8 text Three-dimensional bioprinting technology has emerged with unique advantages for printing biocompatible materials, cells, and other compounds in 3D constructs for cartilage repair or regeneration. Up t [325, 1407, 1125, 1535] body_paragraph 0.6 ["default body_paragraph for text label", "promoted in tail spread from body_paragraph"] body_paragraph 0.6 body_zone body_like none True True
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179 26 2 abstract each individual's anatomy. Therefore, 3D-bioprinted cartilage holds great significance in the research and application for cartilage defect repair and regeneration. [327, 175, 1123, 226] abstract_body 0.85 ["abstract label from Paddle OCR"] abstract_body 0.85 tail_nonref_hold_zone body_like none True True
180 26 3 abstract Three-dimensional bioprinting has the potential to produce bioprinted constructs capable of cell growth, proliferation, and differentiation, along with controllable mechanical and physical properties. [325, 227, 1125, 604] abstract_body 0.85 ["abstract label from Paddle OCR"] abstract_body 0.85 tail_nonref_hold_zone body_like none True True
181 26 4 abstract Another issue that needs to be faced is the hypertrophy of chondrocytes or tissue fibrosis during cartilage tissue repair. In order to prevent the hypertrophy or fibrosis of the chondrocytes produced [326, 603, 1125, 855] abstract_body 0.85 ["abstract label from Paddle OCR"] abstract_body 0.85 tail_nonref_hold_zone body_like none True True
182 26 5 abstract Three-dimensional bioprinting is a promising approach and is considered as a vital tool in the repair or regeneration of cartilage tissue, thus holding the promise to improve the quality of life for p [326, 855, 1125, 1209] abstract_body 0.85 ["abstract label from Paddle OCR"] abstract_body 0.85 tail_nonref_hold_zone body_like none True True
183 26 6 abstract Funding: This work was supported by Health@InnoHK, Innovation and Technology Commission, the Re-search Grant Council (GRF14121815), Hong Kong SAR, the Shenzhen Virtue University Park La-boratory Suppo [327, 1227, 1124, 1324] abstract_body 0.85 ["abstract label from Paddle OCR"] abstract_body 0.85 tail_nonref_hold_zone unknown_like none True True
184 26 7 text Acknowledgments: We are grateful for the support from the Core Laboratories at the School of Biomedical Sciences, Faculty of Medicine, The Chinese University of Hong Kong. [327, 1334, 1123, 1383] body_paragraph 0.6 ["default body_paragraph for text label", "tail_nonref_hold_zone excluded from body flow", "tail_nonref_hold_zone excluded from body flow"] backmatter_body 0.6 tail_nonref_hold_zone unknown_like none True True
185 26 8 text Conflicts of Interest: The authors declare no conflict of interest. [328, 1393, 840, 1416] body_paragraph 0.6 ["default body_paragraph for text label", "tail_nonref_hold_zone excluded from body flow", "tail_nonref_hold_zone excluded from body flow"] backmatter_body 0.6 tail_nonref_hold_zone support_like none True True
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188 27 2 paragraph_title References [67, 176, 175, 199] reference_heading 0.9 ["references heading: References"] reference_heading 0.9 reference_zone heading_like short_fragment True True
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1,5,header_image,,"[896, 108, 1012, 248]",non_body_insert,0.2,"[""unrecognized label 'header_image'""]",unknown_structural,0.2,frontmatter_main_zone,support_like,empty,False,False
1,6,text,Review article,"[75, 287, 202, 309]",non_body_insert,0.3,"[""short text, uncertain role""]",unknown_structural,0.3,frontmatter_main_zone,support_like,short_fragment,False,False
1,7,doc_title,Electrical stimulation for cartilage tissue engineering - A critical review from an engineer’s perspective,"[73, 324, 844, 389]",paper_title,0.6,"[""page-1 frontmatter title guard: Electrical stimulation for cartilage tissue engineering - A ""]",paper_title,0.6,frontmatter_main_zone,support_like,none,True,True
1,8,text,"Julius Zimmermann $ ^{a} $, Abdul Razzaq Farooqi $ ^{a,b,*} $, Ursula van Rienen $ ^{a,c,d} $","[73, 401, 752, 428]",authors,0.8,"[""page-1 zone author_zone: Julius Zimmermann $ ^{a} $, Abdul Razzaq Farooqi $ ^{a,b,*} ""]",authors,0.8,frontmatter_main_zone,support_like,none,True,True
1,9,text," $ ^{a} $ Institute of General Electrical Engineering, University of Rostock, 18051 Rostock, Germany","[73, 434, 577, 451]",affiliation,0.8,"[""page-1 zone affiliation_zone: $ ^{a} $ Institute of General Electrical Engineering, Univer""]",affiliation,0.8,frontmatter_main_zone,support_like,affiliation_marker,True,True
1,10,text," $ ^{b} $ Department of Electronic Engineering, Faculty of Engineering, The Islamia University of Bahawalpur, 63100 Bahawalpur, Pakistan","[73, 451, 788, 469]",affiliation,0.8,"[""page-1 zone affiliation_zone: $ ^{b} $ Department of Electronic Engineering, Faculty of En""]",affiliation,0.8,frontmatter_main_zone,support_like,affiliation_marker,True,True
1,11,text," $ ^{2} $ Department of Ageing of Individuals and Society, Interdisciplinary Faculty, University of Rostock, 18051 Rostock, Germany","[73, 469, 751, 485]",affiliation,0.8,"[""page-1 zone affiliation_zone: $ ^{2} $ Department of Ageing of Individuals and Society, In""]",affiliation,0.8,frontmatter_main_zone,support_like,affiliation_marker,True,True
1,12,text," $ ^{d} $ Department Life, Light & Matter, University of Rostock, 18051 Rostock, Germany","[74, 486, 528, 503]",affiliation,0.8,"[""page-1 zone affiliation_zone: $ ^{d} $ Department Life, Light & Matter, University of Rost""]",affiliation,0.8,frontmatter_main_zone,support_like,affiliation_marker,True,True
1,13,paragraph_title,ARTICLE INFO,"[74, 547, 292, 569]",unknown_structural,0.6,"[""author byline on page 1, assigned as authors: ARTICLE INFO""]",authors,0.6,frontmatter_main_zone,heading_like,short_fragment,False,True
1,14,text,"Keywords:
Articular cartilage
Cartilage tissue engineering
Electrical stimulation
Stimulation devices
Numerical simulations
Biophysical mechanisms","[74, 589, 238, 711]",frontmatter_noise,0.7,"[""frontmatter noise text: Keywords:\nArticular cartilage\nCartilage tissue engineering\nE""]",frontmatter_noise,0.7,body_zone,body_like,none,False,False
1,15,paragraph_title,A B S T R A C T,"[391, 547, 544, 569]",section_heading,0.5,"[""unnumbered paragraph_title on page 1 outside title zone: A B S T R A C T""]",section_heading,0.5,body_zone,heading_like,short_fragment,True,True
1,16,abstract,"Cartilage has a limited intrinsic healing capacity. Hence, cartilage degradation and lesions pose a huge clinical challenge, particularly in an ageing society. Osteoarthritis impacts a significant num","[389, 589, 1016, 840]",abstract_body,0.85,"[""abstract label from Paddle OCR""]",abstract_body,0.85,body_zone,body_like,none,True,True
1,17,paragraph_title,1. Introduction,"[75, 943, 200, 963]",section_heading,0.85,"[""paragraph_title label with numbering: 1. Introduction""]",section_heading,0.85,body_zone,body_like,heading_numbered,True,True
1,18,text,"Cartilage is found in the human body as hyaline, elastic, and fibrocartilage, each of which performs different functions [1]. Among the three cartilaginous tissues, hyaline cartilage is of great clini","[72, 984, 1016, 1174]",body_paragraph,0.6,"[""default body_paragraph for text label""]",body_paragraph,0.6,body_zone,body_like,none,True,True
1,19,footnote," $ ^{*} $ Corresponding author at: Institute of General Electrical Engineering, Faculty of Computer Science and Electrical Engineering, University of Rostock, Albert Einstein Strasse 2, 18059, Rostock","[72, 1211, 1012, 1247]",footnote,0.7,"[""footnote label: $ ^{*} $ Corresponding author at: Institute of General Elect""]",footnote,0.7,body_zone,body_like,affiliation_marker,True,True
1,20,footnote,"E-mail addresses: abdul.farooqi@uni-rostock.de (A.R. Farooqi), ursula.van-rienen@uni-rostock.de (U. van Rienen).","[96, 1247, 748, 1265]",footnote,0.7,"[""footnote label: E-mail addresses: abdul.farooqi@uni-rostock.de (A.R. Farooqi""]",footnote,0.7,body_zone,body_like,none,True,True
1,21,footer,"https://doi.org/10.1016/j.heliyon.2024.e38112
Received 5 April 2024; Received in revised form 31 August 2024; Accepted 18 September 2024","[72, 1282, 690, 1321]",noise,0.9,"[""footer label""]",noise,0.9,body_zone,body_like,none,False,False
1,22,footer,"Available online 23 September 2024
2405-8440/© 2024 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).","[72, 1331, 1014, 1386]",noise,0.9,"[""footer label""]",noise,0.9,body_zone,body_like,none,False,False
2,0,header,"J. Zimmermann, A.R. Farooqi and U. van Rienen","[74, 67, 344, 84]",noise,0.9,"[""header label""]",noise,0.9,frontmatter_side_zone,support_like,none,False,False
2,1,header,Heliyon 10 (2024) e38112,"[864, 67, 1016, 84]",noise,0.9,"[""header label""]",noise,0.9,frontmatter_side_zone,support_like,none,False,False
2,2,image,,"[321, 98, 768, 374]",media_asset,0.85,"[""media label: image""]",media_asset,0.85,body_zone,unknown_like,empty,True,True
2,3,figure_title,Fig. 1. Summary of the cartilage regeneration using the tissue engineering approach. Cells are expanded and seeded on a scaffold before implantation at the defect site. During cell cultivation in a mo,"[73, 392, 1012, 462]",figure_caption,0.92,"[""figure_title label: Fig. 1. Summary of the cartilage regeneration using the tiss""]",figure_caption,0.92,display_zone,legend_like,figure_number,True,True
2,4,text,"At first glance, the engineering of cartilage tissue might appear straightforward: only one type of cell is present while there is no vascular network in cartilage. Moreover, the shape of the tissue t","[72, 486, 1013, 548]",body_paragraph,0.6,"[""default body_paragraph for text label""]",body_paragraph,0.6,body_zone,body_like,none,True,True
2,5,text,Electrical stimulation can be applied in two possible scenarios: monolayer or two-dimensional (2D) and three-dimensional (3D). Both of these scenarios are important. The monolayer chondrocyte loses it,"[72, 549, 1014, 630]",body_paragraph,0.6,"[""default body_paragraph for text label""]",body_paragraph,0.6,body_zone,body_like,none,True,True
2,6,text,"Approaches to engineer cartilage have focused on various scaffold materials, cells and biological or biophysical stimulation signals [9]. In recent years, matrix-assisted autologous chondrocyte implan","[72, 632, 1016, 797]",body_paragraph,0.6,"[""default body_paragraph for text label""]",body_paragraph,0.6,body_zone,body_like,none,True,True
2,7,text,3D cell culture approaches usually make use of scaffolds. Hydrogels as scaffold material resemble the native extracellular matrix $ [16] $ and have thus become a popular choice. They can be seeded wi,"[72, 798, 1015, 881]",body_paragraph,0.6,"[""default body_paragraph for text label""]",body_paragraph,0.6,body_zone,body_like,none,True,True
2,8,text,"A challenge for the engineering of hyaline cartilage is that the produced tissue does often not have the desired mechanical properties but resembles fibrocartilage [6,9,20]. Thus, it is paramount to d","[72, 882, 1016, 1006]",body_paragraph,0.6,"[""default body_paragraph for text label""]",body_paragraph,0.6,body_zone,body_like,none,True,True
2,9,text,"Electromagnetic stimulation of cartilage can be divided into two approaches: pulsed electromagnetic field (PEMF) stimulation and electrical stimulation. In PEMF, an externally applied magnetic field i","[71, 1006, 1015, 1129]",body_paragraph,0.6,"[""default body_paragraph for text label""]",body_paragraph,0.6,body_zone,body_like,none,True,True
2,10,text,"Electrical stimulation has also been considered for regenerative medicine [27,28] in fields such as cardiac tissue engineering [29], wound healing [30–32], bone regeneration [33] or neural stimulation","[72, 1131, 1016, 1317]",body_paragraph,0.6,"[""default body_paragraph for text label""]",body_paragraph,0.6,body_zone,body_like,none,True,True
2,11,text,The aim of this paper is to explain stimulation approaches for cartilage tissue engineering using electrical stimulation from an engineer's perspective. We will summarise the status quo such that read,"[73, 1318, 1015, 1381]",body_paragraph,0.6,"[""default body_paragraph for text label""]",body_paragraph,0.6,body_zone,body_like,none,True,True
2,12,number,2,"[538, 1404, 550, 1418]",noise,0.9,"[""page number label""]",noise,0.9,body_zone,body_like,short_fragment,False,False
3,0,header,"J. Zimmermann, A.R. Farooqi and U. van Rienen","[74, 67, 344, 83]",noise,0.9,"[""header label""]",noise,0.9,body_zone,body_like,none,False,False
3,1,header,Heliyon 10 (2024) e38112,"[864, 67, 1016, 83]",noise,0.9,"[""header label""]",noise,0.9,body_zone,body_like,none,False,False
3,2,text,"which permit a precise and reliable estimation of the stimulating electric field. Finally, we conclude with an overview of biophysical theories that can be used to develop hypotheses for the stimulati","[72, 103, 1014, 166]",body_paragraph,0.6,"[""default body_paragraph for text label""]",body_paragraph,0.6,body_zone,body_like,none,True,True
3,3,paragraph_title,2. The use of electric fields in cartilage engineering and repair,"[73, 187, 559, 208]",section_heading,0.85,"[""paragraph_title label with numbering: 2. The use of electric fields in cartilage engineering and r""]",section_heading,0.85,body_zone,reference_like,reference_numeric_dot,True,True
3,4,text,Electrical stimulation for cartilage tissue engineering has a long tradition dating back to the 1970s [41]. This section provides an overview of the different in vitro and in vivo studies and their re,"[72, 229, 1014, 313]",body_paragraph,0.6,"[""default body_paragraph for text label""]",body_paragraph,0.6,body_zone,body_like,none,True,True
3,5,paragraph_title,2.1. In vitro stimulation,"[74, 334, 244, 353]",subsection_heading,0.85,"[""paragraph_title label with numbering: 2.1. In vitro stimulation""]",subsection_heading,0.85,body_zone,body_like,heading_numbered,True,True
3,6,text,"Many in vitro studies have focused on the influence of capacitive coupling stimulation. Brighton's group is considered a pioneer in this research area and developed a stimulation protocol [42,43], whi","[72, 376, 1015, 625]",body_paragraph,0.6,"[""default body_paragraph for text label""]",body_paragraph,0.6,body_zone,body_like,none,True,True
3,7,text,"Another goal of electrical stimulation is to induce chondrocyte migration and alignment. For example, direct contact electrical stimulation has been investigated for galvanotactic $ [50] $ or dielect","[73, 627, 1014, 690]",body_paragraph,0.6,"[""default body_paragraph for text label""]",body_paragraph,0.6,body_zone,body_like,none,True,True
3,8,paragraph_title,2.2. In vivo stimulation,"[74, 711, 239, 730]",subsection_heading,0.85,"[""paragraph_title label with numbering: 2.2. In vivo stimulation""]",subsection_heading,0.85,body_zone,body_like,heading_numbered,True,True
3,9,text,"Direct contact electric stimulation was applied for cartilage repair in rats [53] and rabbits [54]. Based on the initial investigations of Leppiello et al. [54], gel-covered electrodes were placed on ","[72, 753, 1016, 898]",body_paragraph,0.6,"[""default body_paragraph for text label""]",body_paragraph,0.6,body_zone,body_like,none,True,True
3,10,text,"Systematic reviews of clinical studies on the application of electrical stimulation for the treatment of osteoarthritis have revealed a positive effect regarding improved function, but further relevan","[72, 899, 1015, 1003]",body_paragraph,0.6,"[""default body_paragraph for text label""]",body_paragraph,0.6,body_zone,body_like,none,True,True
3,11,text,"In short, in vitro experiments target a mechanistic understanding of electrical stimulation for cartilage tissue engineering. The goal is to generate cartilage-like tissue structures, e.g., based on h","[72, 1004, 1014, 1108]",body_paragraph,0.6,"[""default body_paragraph for text label""]",body_paragraph,0.6,body_zone,body_like,none,True,True
3,12,paragraph_title,3. Comparison and characterisation of stimulation devices,"[73, 1130, 528, 1150]",section_heading,0.85,"[""paragraph_title label with numbering: 3. Comparison and characterisation of stimulation devices""]",section_heading,0.85,body_zone,reference_like,reference_numeric_dot,True,True
3,13,text,Well-controlled in vitro experiments are needed to comprehensively investigate the interaction between applied stimulation and the cells [64]. The in vitro experiments should be replicable to ensure t,"[72, 1171, 1015, 1337]",body_paragraph,0.6,"[""default body_paragraph for text label""]",body_paragraph,0.6,body_zone,body_like,none,True,True
3,14,text,"This section focuses on the electrical characteristics of the typical stimulation techniques in cartilage tissue engineering. In general, two types of stimulation are prevalent: direct contact and cap","[72, 1338, 1014, 1381]",body_paragraph,0.6,"[""default body_paragraph for text label""]",body_paragraph,0.6,body_zone,body_like,none,True,True
3,15,number,3,"[538, 1404, 550, 1418]",noise,0.9,"[""page number label""]",noise,0.9,body_zone,body_like,short_fragment,False,False
4,0,header,"J. Zimmermann, A.R. Farooqi and U. van Rienen","[74, 67, 344, 84]",noise,0.9,"[""header label""]",noise,0.9,body_zone,body_like,none,False,False
4,1,header,Heliyon 10 (2024) e38112,"[864, 67, 1016, 83]",noise,0.9,"[""header label""]",noise,0.9,body_zone,body_like,none,False,False
4,2,image,,"[278, 96, 810, 235]",media_asset,0.85,"[""media label: image""]",media_asset,0.85,body_zone,unknown_like,empty,True,True
4,3,chart,,"[278, 265, 784, 487]",media_asset,0.85,"[""media label: chart""]",media_asset,0.85,body_zone,unknown_like,empty,True,True
4,4,chart,,"[278, 507, 797, 659]",media_asset,0.85,"[""media label: chart""]",media_asset,0.85,body_zone,unknown_like,empty,True,True
4,5,figure_title,Fig. 2. Comparison of the most common waveforms used for electrical stimulation in the context of cartilage tissue engineering. All shown signals are periodic signals. The monophasic and biphasic puls,"[73, 678, 1012, 751]",figure_caption,0.92,"[""figure_title label: Fig. 2. Comparison of the most common waveforms used for ele""]",figure_caption,0.92,display_zone,legend_like,figure_number,True,True
4,6,text,"of a thin, dielectric, non-conducting layer between the electrodes and the sample, while direct contact refers to the absence of the dielectric layer. A voltage is applied to the electrodes, resulting","[72, 793, 1016, 1006]",body_paragraph,0.6,"[""default body_paragraph for text label""]",body_paragraph,0.6,body_zone,body_like,none,True,True
4,7,paragraph_title,3.1. Physiological electric fields and current densities,"[73, 1031, 436, 1052]",subsection_heading,0.85,"[""paragraph_title label with numbering: 3.1. Physiological electric fields and current densities""]",subsection_heading,0.85,body_zone,body_like,heading_numbered,True,True
4,8,text,"To give the reader a perspective, we will first briefly outline the physiologically expected electric fields and current densities. A first estimate of the physiologically expected electric field stre","[71, 1080, 1017, 1335]",body_paragraph,0.6,"[""default body_paragraph for text label""]",body_paragraph,0.6,body_zone,body_like,none,True,True
4,9,display_formula, $$ E=\frac{J}{\sigma}=\frac{1\ m A\ cm^{-2}}{1\ S m^{-1}}=\frac{10\ A m^{-2}}{1\ S m^{-1}}=10\ V m^{-1}\ . $$ ,"[121, 1343, 455, 1388]",unknown_structural,0.2,"[""unrecognized label 'display_formula'""]",unknown_structural,0.2,body_zone,body_like,none,False,True
4,10,formula_number,(1),"[988, 1357, 1012, 1376]",unknown_structural,0.2,"[""unrecognized label 'formula_number'""]",unknown_structural,0.2,body_zone,body_like,short_fragment,False,True
4,11,number,4,"[538, 1404, 550, 1418]",noise,0.9,"[""page number label""]",noise,0.9,body_zone,body_like,short_fragment,False,False
5,0,header,"J. Zimmermann, A.R. Farooqi and U. van Rienen","[74, 67, 344, 83]",noise,0.9,"[""header label""]",noise,0.9,body_zone,body_like,none,False,False
5,1,header,Heliyon 10 (2024) e38112,"[864, 67, 1016, 83]",noise,0.9,"[""header label""]",noise,0.9,body_zone,body_like,none,False,False
5,2,text,This exemplary calculation based on the literature data shows that the electric field amplitude that comes closest to the presumed physiological electric field amplitude should be in the order of $ 1,"[72, 103, 1014, 188]",body_paragraph,0.6,"[""default body_paragraph for text label""]",body_paragraph,0.6,body_zone,body_like,none,True,True
5,3,paragraph_title,3.2. Overview of direct contact stimulation approaches,"[73, 209, 449, 228]",subsection_heading,0.85,"[""paragraph_title label with numbering: 3.2. Overview of direct contact stimulation approaches""]",subsection_heading,0.85,body_zone,body_like,heading_numbered,True,True
5,4,text,Direct contact electrical stimulation is the most comprehensible approach as the electrodes are brought in direct contact with the sample and a stimulus is applied (see also the scheme in Fig. 3A).,"[73, 251, 1014, 291]",body_paragraph,0.6,"[""default body_paragraph for text label""]",body_paragraph,0.6,body_zone,body_like,none,True,True
5,5,text,"Among the waveforms under consideration, rectangular pulses have been the subject of in vivo studies. For example, a current-controlled 20 $ \mu $A biphasic rectangular pulse of width 10 ms having 0.","[71, 293, 1016, 647]",body_paragraph,0.6,"[""default body_paragraph for text label""]",body_paragraph,0.6,body_zone,body_like,none,True,True
5,6,text,Another type of stimulation using rectangular signals is nano-second pulsed electrical field (nsPEF) stimulation. High voltage electric signals leading to field strengths in the range of kV cm $ ^{-1},"[72, 649, 1016, 815]",body_paragraph,0.6,"[""default body_paragraph for text label""]",body_paragraph,0.6,body_zone,body_like,none,True,True
5,7,text,"Aside from rectangular pulses, sinusoidal waves of low frequency have been considered for direct contact stimulation. e.g., 1 kHz for the in vitro stimulation of chondrocytes [85] while 20 Hz are comm","[72, 817, 1017, 1192]",body_paragraph,0.6,"[""default body_paragraph for text label""]",body_paragraph,0.6,body_zone,body_like,none,True,True
5,8,text,Experimental approaches in tissue engineering also exist that use DC signals [36]. The direct current is mainly the result of faradaic electrochemical reactions [97]. Agar-salt bridges are usually emp,"[72, 1192, 1017, 1381]",body_paragraph,0.6,"[""default body_paragraph for text label""]",body_paragraph,0.6,body_zone,body_like,none,True,True
5,9,number,5,"[538, 1404, 550, 1418]",noise,0.9,"[""page number label""]",noise,0.9,body_zone,body_like,short_fragment,False,False
6,0,header,"J. Zimmermann, A.R. Farooqi and U. van Rienen","[74, 67, 344, 83]",noise,0.9,"[""header label""]",noise,0.9,body_zone,body_like,none,False,False
6,1,header,Heliyon 10 (2024) e38112,"[864, 67, 1016, 83]",noise,0.9,"[""header label""]",noise,0.9,body_zone,body_like,none,False,False
6,2,text,"voltage but no active stimulator was used, i.e., the stimulation was passive. In contrast, DC stimulation of chondrocytes in 3D agarose culture has not led to a significant response [100].","[73, 103, 1013, 144]",body_paragraph,0.6,"[""default body_paragraph for text label""]",body_paragraph,0.6,body_zone,body_like,none,True,True
6,3,text,All the aforementioned in vitro and in vivo studies of direct contact electrical stimulation are summarised in Table 1.,"[96, 146, 933, 165]",body_paragraph,0.6,"[""default body_paragraph for text label""]",body_paragraph,0.6,body_zone,body_like,none,True,True
6,4,paragraph_title,3.3. Capacitive coupling stimulation,"[74, 187, 324, 208]",subsection_heading,0.85,"[""paragraph_title label with numbering: 3.3. Capacitive coupling stimulation""]",subsection_heading,0.85,body_zone,body_like,heading_numbered,True,True
6,5,text,"At first, capacitive coupling seems conflicting because no direct current flows between the sample and stimulation electrodes as the current is blocked due to an insulating layer on the stimulation el","[72, 230, 1014, 353]",body_paragraph,0.6,"[""default body_paragraph for text label""]",body_paragraph,0.6,body_zone,body_like,none,True,True
6,6,text,"For cartilage tissue engineering and regeneration involving capacitive coupling, most of the work has been performed by Brighton's group since the 1980's [42,44,45,103,110–112]. Other researchers have","[73, 355, 1015, 457]",body_paragraph,0.6,"[""default body_paragraph for text label""]",body_paragraph,0.6,body_zone,body_like,none,True,True
6,7,text,"In 2004, a study was reported by Brighton's group using 30 kHz and 120 kHz frequencies for capacitive stimulation [44]. The bovine articular chondrocytes were utilized to investigate the effect of ele","[72, 460, 1016, 626]",body_paragraph,0.6,"[""default body_paragraph for text label""]",body_paragraph,0.6,body_zone,body_like,none,True,True
6,8,text,"in recent years, other frequencies such as 1 kHz [15,47,114] or 4150 kHz [115] have been considered. Among these studies, a novel concept of coupling the fields not via a plastic layer but by niobium ","[72, 627, 1016, 752]",body_paragraph,0.6,"[""default body_paragraph for text label""]",body_paragraph,0.6,body_zone,body_like,none,True,True
6,9,text,"To our knowledge, only one study uses capacitive electric stimulation of human cells describing an experimental characterisation approach $ [117] $. The authors have proposed an equivalent circuit, r","[72, 753, 1014, 877]",body_paragraph,0.6,"[""default body_paragraph for text label""]",body_paragraph,0.6,body_zone,body_like,none,True,True
6,10,text,All studies of capacitive electrical stimulation are summarised in Table 2.,"[97, 878, 624, 897]",body_paragraph,0.6,"[""default body_paragraph for text label""]",body_paragraph,0.6,body_zone,body_like,none,True,True
6,11,paragraph_title,3.4. Electrochemical analysis,"[74, 920, 278, 940]",subsection_heading,0.85,"[""paragraph_title label with numbering: 3.4. Electrochemical analysis""]",subsection_heading,0.85,body_zone,body_like,heading_numbered,True,True
6,12,text,"In the following, all necessary techniques to estimate the electric field strength of the stimulation reliably will be compiled. Firstly, the focus is on direct contact stimulation. Please note that d","[71, 962, 1015, 1170]",body_paragraph,0.6,"[""default body_paragraph for text label""]",body_paragraph,0.6,body_zone,body_like,none,True,True
6,13,text,"The impedance is impacted by multiple parameters. For example, a suitable electrode material can reduce unwanted electrochemical reactions. Unfortunately, it is not possible to reliably predict electr","[72, 1171, 1017, 1381]",body_paragraph,0.6,"[""default body_paragraph for text label""]",body_paragraph,0.6,body_zone,body_like,none,True,True
6,14,number,6,"[538, 1405, 550, 1418]",noise,0.9,"[""page number label""]",noise,0.9,body_zone,body_like,short_fragment,False,False
7,0,figure_title,Electrical stimulation studies of articular cartilage using direct coupling. Please note that consistent units were used (either kV m $ ^{-1} $ or mV cm $ ^{-1} $) to make the studies comparable.,"[106, 89, 1121, 112]",figure_caption_candidate,0.85,"[""figure_title label: Electrical stimulation studies of articular cartilage using ""]",figure_caption,0.85,body_zone,legend_like,none,False,False
7,1,table,<table><tr><td>Study</td><td>Year</td><td>Stimulation protocol</td><td>Electric Field Strength</td><td>Voltage</td><td>Frequency</td><td>Current / Current density</td><td>Waveform</td><td>Tissue type ,"[94, 100, 1396, 987]",media_asset,0.85,"[""media label: table""]",media_asset,0.85,body_zone,body_like,none,True,True
7,2,vision_footnote,"* MSCs, mesenchymal stem cells; ADSCs, adipose-derived stem cells; BMMSCs, bone marrow-derived stromal cells; DC, direct current; NR, not reported; NA, not applicable; PW, pulse width; IG, interphase ","[106, 997, 1301, 1017]",footnote,0.7,"[""vision_footnote label: * MSCs, mesenchymal stem cells; ADSCs, adipose-derived stem ""]",footnote,0.7,body_zone,body_like,none,True,True
7,3,aside_text,"J. Zimmermann, A.R. Farooqi and U. van Rienen","[1402, 76, 1419, 342]",unknown_structural,0.2,"[""unrecognized label 'aside_text'""]",unknown_structural,0.2,body_zone,unknown_like,none,False,True
7,4,aside_text,Heliyon 10 (2024) e38112,"[1401, 863, 1421, 1016]",unknown_structural,0.2,"[""unrecognized label 'aside_text'""]",unknown_structural,0.2,body_zone,unknown_like,none,False,True
8,0,figure_title,"Table 2
Electrical stimulation studies of articular cartilage using capacitive coupling.","[107, 204, 548, 240]",table_caption,0.9,"[""table prefix matched: Table 2\nElectrical stimulation studies of articular cartilag""]",table_caption,0.9,display_zone,table_caption_like,table_number,True,True
8,1,table,<table><tr><td>Study</td><td>Year</td><td>Stimulation protocol</td><td>Electric field Strength</td><td>Voltage</td><td>Frequency</td><td>Current / Current density</td><td>Waveform</td><td>Tissue type ,"[106, 234, 1384, 867]",media_asset,0.85,"[""media label: table""]",media_asset,0.85,body_zone,body_like,none,True,True
8,2,number,∞,"[66, 538, 83, 552]",noise,0.9,"[""page number label""]",noise,0.9,body_zone,unknown_like,short_fragment,False,False
8,3,vision_footnote,"MSCs, mesenchymal stem cells; ADSCs, adipose-derived stem cells; DC, direct current; NR, not reported; PW, pulse width; CPW, counterpulse width.","[107, 869, 966, 889]",footnote,0.7,"[""vision_footnote label: MSCs, mesenchymal stem cells; ADSCs, adipose-derived stem ce""]",footnote,0.7,body_zone,body_like,none,True,True
8,4,aside_text,"J. Zimmermann, A.R. Farooqi and U. van Rienen","[1400, 73, 1419, 342]",unknown_structural,0.2,"[""unrecognized label 'aside_text'""]",unknown_structural,0.2,body_zone,unknown_like,none,False,True
8,5,aside_text,Heliyon 10 (2024) e38112,"[1400, 864, 1422, 1016]",unknown_structural,0.2,"[""unrecognized label 'aside_text'""]",unknown_structural,0.2,body_zone,unknown_like,none,False,True
9,0,header,"J. Zimmermann, A.R. Farooqi and U. van Rienen","[74, 67, 344, 83]",noise,0.9,"[""header label""]",noise,0.9,body_zone,body_like,none,False,False
9,1,header,Heliyon 10 (2024) e38112,"[864, 67, 1016, 84]",noise,0.9,"[""header label""]",noise,0.9,body_zone,body_like,none,False,False
9,2,image,,"[166, 99, 547, 382]",media_asset,0.85,"[""media label: image""]",media_asset,0.85,body_zone,unknown_like,empty,True,True
9,3,figure_title,B,"[584, 105, 610, 135]",figure_inner_text,0.9,"[""panel label / figure inner text: B""]",figure_inner_text,0.9,display_zone,legend_like,panel_label,True,True
9,4,image,,"[583, 140, 922, 370]",media_asset,0.85,"[""media label: image""]",media_asset,0.85,body_zone,unknown_like,empty,True,True
9,5,figure_title,"Fig. 3. Summary of electrical stimulation techniques and associated equivalent circuit models. We report the range of estimated stimulation field strengths, stimulation amplitudes, frequencies and wav","[73, 403, 1015, 543]",figure_caption,0.92,"[""figure_title label: Fig. 3. Summary of electrical stimulation techniques and ass""]",figure_caption,0.92,display_zone,legend_like,figure_number,True,True
9,6,text,"be used as an indicator. As an example of this, the transition from capacitive ( $ -80^\circ $) to diffusive ( $ -45^\circ $) characteristics was noticed for a platinum electrode in saline solution th","[72, 569, 1013, 609]",body_paragraph,0.6,"[""default body_paragraph for text label""]",body_paragraph,0.6,body_zone,body_like,none,True,True
9,7,text,"The measured impedance is described by equivalent circuit models, usually consisting of linear elements only. For example, a constant-phase element is used to describe the electrode-tissue interface. ","[72, 611, 1014, 818]",body_paragraph,0.6,"[""default body_paragraph for text label""]",body_paragraph,0.6,body_zone,body_like,none,True,True
9,8,text,The electrical stimulation approaches can be linked to electrochemical characterisation methods. Direct current stimulation at a fixed voltage results in the generation of a time-dependent current. In,"[72, 819, 1015, 943]",body_paragraph,0.6,"[""default body_paragraph for text label""]",body_paragraph,0.6,body_zone,body_like,none,True,True
9,9,text,Alternating current stimulation using sine or periodic waves resembles measuring the impedance if both voltage and current are recorded. Probing the impedance over an extensive frequency range is call,"[71, 945, 1016, 1131]",body_paragraph,0.6,"[""default body_paragraph for text label""]",body_paragraph,0.6,body_zone,body_like,none,True,True
9,10,text,"In short, a general expected equivalent circuit can be formulated for each stimulation type. This permits the establishment of a corresponding electrochemical measurement approach for each stimulation","[73, 1132, 1014, 1195]",body_paragraph,0.6,"[""default body_paragraph for text label""]",body_paragraph,0.6,body_zone,body_like,none,True,True
9,11,paragraph_title,3.5. Towards reliable and controllable electrical stimulation,"[73, 1214, 483, 1235]",subsection_heading,0.85,"[""paragraph_title label with numbering: 3.5. Towards reliable and controllable electrical stimulatio""]",subsection_heading,0.85,body_zone,body_like,heading_numbered,True,True
9,12,text,The comparison of the existing stimulation approaches does not permit to identify a clear formulation of the ‘ideal’ electrical stimulation of cartilage (cf. Tables 1 and 2) despite reports of benefic,"[72, 1255, 1016, 1381]",body_paragraph,0.6,"[""default body_paragraph for text label""]",body_paragraph,0.6,body_zone,body_like,none,True,True
9,13,number,9,"[538, 1404, 550, 1418]",noise,0.9,"[""page number label""]",noise,0.9,body_zone,body_like,short_fragment,False,False
10,0,header,"J. Zimmermann, A.R. Farooqi and U. van Rienen","[74, 67, 344, 83]",noise,0.9,"[""header label""]",noise,0.9,body_zone,body_like,none,False,False
10,1,header,Heliyon 10 (2024) e38112,"[864, 67, 1016, 83]",noise,0.9,"[""header label""]",noise,0.9,body_zone,body_like,none,False,False
10,2,text,"A key ingredient is the impedance spectroscopy of the stimulation system. Ideally, it is conducted with a sample having known properties (e.g., an electrolyte solution) and on the actual sample multip","[72, 104, 1016, 333]",body_paragraph,0.6,"[""default body_paragraph for text label""]",body_paragraph,0.6,body_zone,body_like,none,True,True
10,3,text,"An open question is how to numerically model the electrode-tissue interface layer. One possibility is to assume a voltage divider, in which the interface impedance is described as a lumped element [86","[71, 335, 1016, 647]",body_paragraph,0.6,"[""default body_paragraph for text label""]",body_paragraph,0.6,body_zone,body_like,none,True,True
10,4,text,"In our opinion, the lack of current and voltage recording data (compare Tables 1 and 2) currently marks the biggest problem in the understanding and retrospectively analysing of the electrical stimula","[71, 648, 1017, 1045]",body_paragraph,0.6,"[""default body_paragraph for text label""]",body_paragraph,0.6,body_zone,body_like,none,True,True
10,5,text,"The cellular dielectric properties cover a wide range [144], and their influence on the modelling result needs to be carefully tested [109]. In future research, optical methods combined with impedance","[71, 1046, 1015, 1150]",body_paragraph,0.6,"[""default body_paragraph for text label""]",body_paragraph,0.6,body_zone,body_like,none,True,True
10,6,text,"With this information, more advanced biophysical models can be developed. In the following section, some potential biophysical interactions are introduced. In addition to the biophysical modelling, sa","[72, 1151, 1014, 1234]",body_paragraph,0.6,"[""default body_paragraph for text label""]",body_paragraph,0.6,body_zone,body_like,none,True,True
10,7,paragraph_title,4. Biophysical theories of the interaction of electric fields and cells,"[73, 1254, 594, 1274]",section_heading,0.85,"[""paragraph_title label with numbering: 4. Biophysical theories of the interaction of electric field""]",section_heading,0.85,body_zone,reference_like,reference_numeric_dot,True,True
10,8,text,"Various studies have hypothesised that electric fields can stimulate non-excitable cells, e.g. chondrocytes, to differentiate, proliferate or produce chondrogenic proteins. Still, it is unclear which ","[72, 1297, 1017, 1381]",body_paragraph,0.6,"[""default body_paragraph for text label""]",body_paragraph,0.6,body_zone,body_like,none,True,True
10,9,number,10,"[535, 1404, 554, 1418]",noise,0.9,"[""page number label""]",noise,0.9,body_zone,body_like,short_fragment,False,False
11,0,header,"J. Zimmermann, A.R. Farooqi and U. van Rienen","[74, 67, 343, 83]",noise,0.9,"[""header label""]",noise,0.9,body_zone,body_like,none,False,False
11,1,header,Heliyon 10 (2024) e38112,"[864, 67, 1016, 83]",noise,0.9,"[""header label""]",noise,0.9,body_zone,body_like,none,False,False
11,2,figure_title,A,"[280, 96, 305, 122]",figure_inner_text,0.9,"[""panel label / figure inner text: A""]",figure_inner_text,0.9,display_zone,legend_like,panel_label,True,True
11,3,figure_title,Numerical model,"[470, 105, 613, 125]",figure_caption,0.85,"[""figure_title label: Numerical model""]",figure_caption,0.85,body_zone,unknown_like,short_fragment,True,True
11,4,image,,"[307, 134, 442, 268]",media_asset,0.85,"[""media label: image""]",media_asset,0.85,body_zone,unknown_like,empty,True,True
11,5,figure_title,Equivalent circuit,"[641, 105, 786, 126]",figure_caption,0.85,"[""figure_title label: Equivalent circuit""]",figure_caption,0.85,body_zone,unknown_like,short_fragment,True,True
11,6,image,,"[475, 136, 614, 254]",media_asset,0.85,"[""media label: image""]",media_asset,0.85,body_zone,unknown_like,empty,True,True
11,7,image,,"[634, 153, 792, 246]",media_asset,0.85,"[""media label: image""]",media_asset,0.85,body_zone,unknown_like,empty,True,True
11,8,figure_title,B,"[279, 282, 301, 306]",figure_inner_text,0.9,"[""panel label / figure inner text: B""]",figure_inner_text,0.9,display_zone,legend_like,panel_label,True,True
11,9,image,,"[290, 288, 405, 450]",media_asset,0.85,"[""media label: image""]",media_asset,0.85,body_zone,unknown_like,empty,True,True
11,10,figure_title,C,"[540, 282, 563, 307]",figure_inner_text,0.9,"[""panel label / figure inner text: C""]",figure_inner_text,0.9,display_zone,legend_like,panel_label,True,True
11,11,image,,"[416, 312, 519, 450]",media_asset,0.85,"[""media label: image""]",media_asset,0.85,body_zone,unknown_like,empty,True,True
11,12,image,,"[526, 311, 661, 448]",media_asset,0.85,"[""media label: image""]",media_asset,0.85,body_zone,unknown_like,empty,True,True
11,13,image,,"[676, 309, 810, 449]",media_asset,0.85,"[""media label: image""]",media_asset,0.85,body_zone,unknown_like,empty,True,True
11,14,figure_title,D,"[279, 461, 303, 485]",figure_inner_text,0.9,"[""panel label / figure inner text: D""]",figure_inner_text,0.9,display_zone,legend_like,panel_label,True,True
11,15,figure_title,Integrate cell model,"[446, 464, 629, 486]",figure_caption_candidate,0.85,"[""figure_title label: Integrate cell model""]",figure_caption,0.85,body_zone,unknown_like,none,False,False
11,16,image,,"[310, 475, 796, 603]",media_asset,0.85,"[""media label: image""]",media_asset,0.85,body_zone,unknown_like,empty,True,True
11,17,figure_title,E,"[279, 597, 300, 621]",figure_inner_text,0.9,"[""panel label / figure inner text: E""]",figure_inner_text,0.9,display_zone,legend_like,panel_label,True,True
11,18,image,,"[358, 630, 719, 764]",media_asset,0.85,"[""media label: image""]",media_asset,0.85,body_zone,unknown_like,empty,True,True
11,19,figure_title,"Fig. 4. Future vision of cartilage tissue engineering experiments: Experiments are conducted alongside numerical simulations (panel A, adapted from [72], it is under CC-BY licence (CC BY 4.0)). A nume","[72, 786, 1017, 924]",figure_caption,0.92,"[""figure_title label: Fig. 4. Future vision of cartilage tissue engineering experi""]",figure_caption,0.92,display_zone,legend_like,figure_number,True,True
11,20,paragraph_title,4.1. Induced transmembrane potential,"[75, 957, 339, 976]",subsection_heading,0.85,"[""paragraph_title label with numbering: 4.1. Induced transmembrane potential""]",subsection_heading,0.85,body_zone,body_like,heading_numbered,True,True
11,21,text,"The cell membrane is a lipid bilayer having a thickness of around 7 nm [133], which separates the cell interior (the cytoplasm) from the extracellular medium. Each cell type has a characteristic resti","[73, 1002, 1013, 1064]",body_paragraph,0.6,"[""default body_paragraph for text label""]",body_paragraph,0.6,body_zone,body_like,none,True,True
11,22,text,"Compared to eukaryotic cells, which have a resting transmembrane potential in the range of $ -70 $ mV to $ -40 $ mV [147], chondrocytes have an increased resting potential of $ -46 $ mV to $ -11 $","[73, 1065, 1014, 1168]",body_paragraph,0.6,"[""default body_paragraph for text label""]",body_paragraph,0.6,body_zone,body_like,none,True,True
11,23,text,"Voltage-dependent calcium channels are essential in forming and maintaining healthy cartilage [150]. However, their reaction mechanism to external electrical stimuli is unclear [21]. Electrical stimul","[73, 1169, 1014, 1296]",body_paragraph,0.6,"[""default body_paragraph for text label""]",body_paragraph,0.6,body_zone,body_like,none,True,True
11,24,text,Xu et al. have reported that blockers of voltage-dependent calcium channels inhibit entirely the electrical stimulation effects $ [103] $. No estimate of the induced transmembrane potential in adult ,"[72, 1297, 1013, 1381]",body_paragraph,0.6,"[""default body_paragraph for text label""]",body_paragraph,0.6,body_zone,body_like,none,True,True
11,25,number,11,"[534, 1404, 553, 1418]",noise,0.9,"[""page number label""]",noise,0.9,body_zone,body_like,short_fragment,False,False
12,0,header,"J. Zimmermann, A.R. Farooqi and U. van Rienen","[74, 67, 344, 83]",noise,0.9,"[""header label""]",noise,0.9,body_zone,body_like,none,False,False
12,1,header,Heliyon 10 (2024) e38112,"[864, 67, 1016, 83]",noise,0.9,"[""header label""]",noise,0.9,body_zone,body_like,none,False,False
12,2,text,"When even higher membrane potentials are induced (hundreds of mV), it comes to electropemeabilisation [147]. The membrane becomes porous due to a strong electric field inside the very thin membrane. T","[72, 103, 1014, 165]",body_paragraph,0.6,"[""default body_paragraph for text label""]",body_paragraph,0.6,body_zone,body_like,none,True,True
12,3,text,"One can estimate the lower effective range of the induced transmembrane potential by comparing the drift current to the random diffusion current. The applied electric field causes the drift current, w","[72, 167, 1014, 293]",body_paragraph,0.6,"[""default body_paragraph for text label""]",body_paragraph,0.6,body_zone,body_like,none,True,True
12,4,text,"To summarize, the aforementioned observations indicate that very large induced transmembrane potentials greater than 100 mV should be avoided in cartilage tissue engineering to not destroy the membran","[72, 294, 1014, 417]",body_paragraph,0.6,"[""default body_paragraph for text label""]",body_paragraph,0.6,body_zone,body_like,none,True,True
12,5,text,"There exist analytical [157–159] as well as numerical techniques [160] to estimate the transmembrane potential [109,145]. An experimental validation of the estimated values has been presented in [160,","[72, 420, 1016, 610]",body_paragraph,0.6,"[""default body_paragraph for text label""]",body_paragraph,0.6,body_zone,body_like,none,True,True
12,6,paragraph_title,4.2. Electromechanical interaction,"[74, 634, 313, 653]",subsection_heading,0.85,"[""paragraph_title label with numbering: 4.2. Electromechanical interaction""]",subsection_heading,0.85,body_zone,body_like,heading_numbered,True,True
12,7,text,"The electromechanical interaction of electrical stimulation can occur at the cellular scale, the membrane scale, or even the protein scale.","[72, 679, 1014, 719]",body_paragraph,0.6,"[""default body_paragraph for text label""]",body_paragraph,0.6,body_zone,body_like,none,True,True
12,8,paragraph_title,4.2.1. Cell migration: dielectrophoresis and galvanotaxis,"[72, 745, 461, 764]",subsection_heading,0.85,"[""paragraph_title label with numbering: 4.2.1. Cell migration: dielectrophoresis and galvanotaxis""]",subsection_heading,0.85,body_zone,body_like,heading_numbered,True,True
12,9,text,"Cell migration due to an external electric field is referred to as electrotaxis or galvanotaxis (direct current fields) and dielectrophoresis (alternating current fields) [162–164]. Moreover, cells ma","[72, 766, 1016, 849]",body_paragraph,0.6,"[""default body_paragraph for text label""]",body_paragraph,0.6,body_zone,body_like,none,True,True
12,10,text,"In electrotaxis, the electric field functions as a compass for the cells [162]. The electric field does not control the cells in a defined direction compared to electrophoresis. Instead, electrotaxis ","[72, 851, 1016, 1018]",body_paragraph,0.6,"[""default body_paragraph for text label""]",body_paragraph,0.6,body_zone,body_like,none,True,True
12,11,text,"Cells can also migrate due to dielectrophoresis upon alternating current electrical stimulation. In contrast to galvanotaxis, dielectrophoresis is explained by classical electrodynamics [163]. The for","[72, 1020, 1016, 1188]",body_paragraph,0.6,"[""default body_paragraph for text label""]",body_paragraph,0.6,body_zone,body_like,none,True,True
12,12,paragraph_title,4.2.2. Electro-deformation,"[74, 1211, 262, 1230]",subsection_heading,0.85,"[""paragraph_title label with numbering: 4.2.2. Electro-deformation""]",subsection_heading,0.85,body_zone,body_like,heading_numbered,True,True
12,13,text,"Even though homogeneous electric fields can deform the spherical cells, they cannot induce migration of these cells [166,167]. It has been reported that MSCs embedded in hydrogels stimulated by the ca","[72, 1232, 1015, 1381]",body_paragraph,0.6,"[""default body_paragraph for text label""]",body_paragraph,0.6,body_zone,body_like,none,True,True
12,14,number,12,"[535, 1404, 554, 1418]",noise,0.9,"[""page number label""]",noise,0.9,body_zone,body_like,short_fragment,False,False
13,0,header,"J. Zimmermann, A.R. Farooqi and U. van Rienen","[74, 67, 344, 83]",noise,0.9,"[""header label""]",noise,0.9,body_zone,body_like,none,False,False
13,1,header,Heliyon 10 (2024) e38112,"[864, 66, 1016, 83]",noise,0.9,"[""header label""]",noise,0.9,body_zone,body_like,none,False,False
13,2,text,"To explain the electro-deformation phenomenon of cells, giant vesicles (cell-like constructs usually having no nucleus) have been studied $ [169] $. The morphological changes in alternating current f","[72, 103, 1014, 252]",body_paragraph,0.6,"[""default body_paragraph for text label""]",body_paragraph,0.6,body_zone,body_like,none,True,True
13,3,paragraph_title,4.2.3. Interaction with membrane constituents,"[73, 275, 393, 293]",subsection_heading,0.85,"[""paragraph_title label with numbering: 4.2.3. Interaction with membrane constituents""]",subsection_heading,0.85,body_zone,body_like,heading_numbered,True,True
13,4,text,"The effect of oscillating electric fields could be related to electroconformational coupling, which has been explored since the late 1980s [175]. However, the effect has not been visually observed but","[72, 295, 1013, 421]",body_paragraph,0.6,"[""default body_paragraph for text label""]",body_paragraph,0.6,body_zone,body_like,none,True,True
13,5,text,"The biological effect of the electroconformational coupling has been linked to H⁺-ATPase membrane pumps [176], also affected by the membrane potential [182]. Sodium potassium pumps (Na⁺/K⁺-ATPase) are","[73, 422, 1013, 485]",body_paragraph,0.6,"[""default body_paragraph for text label""]",body_paragraph,0.6,body_zone,body_like,none,True,True
13,6,text,"Another open research topic is the motility of membrane constituents. For example, lipid rafts have been shown to cluster upon electrical stimulation in fibroblasts [185]. At extremely low frequencies","[72, 486, 1015, 589]",body_paragraph,0.6,"[""default body_paragraph for text label""]",body_paragraph,0.6,body_zone,body_like,none,True,True
13,7,text,"Moreover, chondrocytes also express Piezo channels [188,189] or TRPV4 [190] mechanosensitive membrane channels. The mechanism of interaction of the membrane and its constituents with electrical stimul","[71, 590, 1017, 803]",body_paragraph,0.6,"[""default body_paragraph for text label""]",body_paragraph,0.6,body_zone,body_like,none,True,True
13,8,paragraph_title,5. Summary and outlook,"[74, 824, 272, 844]",section_heading,0.85,"[""paragraph_title label with numbering: 5. Summary and outlook""]",section_heading,0.85,body_zone,reference_like,reference_numeric_dot,True,True
13,9,text,"We discussed electrical stimulation as a biophysical intervention for the repair and regeneration of cartilage. Despite decades of research, it appears that no reliable electrical stimulation protocol","[71, 869, 1016, 1080]",body_paragraph,0.6,"[""default body_paragraph for text label""]",body_paragraph,0.6,body_zone,body_like,none,True,True
13,10,text,We believe that improved reporting standards focusing on technical replicability combined with continuous monitoring of stimulation parameters will eventually enable a more effective determination of ,"[71, 1080, 1016, 1228]",body_paragraph,0.6,"[""default body_paragraph for text label""]",body_paragraph,0.6,body_zone,body_like,none,True,True
13,11,paragraph_title,CRediT authorship contribution statement,"[74, 1251, 404, 1272]",backmatter_heading,0.8,"[""backmatter heading on page 13: CRediT authorship contribution statement""]",backmatter_heading_candidate,0.8,body_zone,body_like,none,True,True
13,12,text,"Julius Zimmermann: Writing – review & editing, Writing – original draft, Visualization, Validation, Software, Resources, Methodology, Investigation, Formal analysis, Data curation, Conceptualization. ","[72, 1296, 1014, 1381]",backmatter_body,0.6,"[""default body_paragraph for text label""]",body_paragraph,0.6,body_zone,body_like,none,True,True
13,13,number,13,"[535, 1404, 554, 1418]",noise,0.9,"[""page number label""]",noise,0.9,body_zone,body_like,short_fragment,False,False
14,0,header,"J. Zimmermann, A.R. Farooqi and U. van Rienen","[75, 67, 344, 84]",noise,0.9,"[""header label""]",noise,0.9,body_zone,body_like,none,False,False
14,1,header,Heliyon 10 (2024) e38112,"[864, 66, 1016, 84]",noise,0.9,"[""header label""]",noise,0.9,body_zone,body_like,none,False,False
14,2,text,The authors declare that they have no known competing financial interests or personal relationships that could have appeared to uence the work reported in this paper.,"[102, 146, 1020, 188]",body_paragraph,0.6,"[""default body_paragraph for text label""]",body_paragraph,0.6,body_zone,body_like,none,True,True
14,3,text,The authors are grateful to Rainer Bader for pointing out the need for research into the appropriate modelling of electrical stimulation of cartilage tissue. We thank Simone Krüger and Nils Arbeiter f,"[72, 252, 1014, 316]",backmatter_body,0.6,"[""default body_paragraph for text label"", ""promoted in tail spread from body_paragraph""]",body_paragraph,0.6,body_zone,body_like,none,True,True
14,4,paragraph_title,References,"[74, 337, 164, 357]",reference_heading,0.9,"[""references heading: References""]",reference_heading,0.9,reference_zone,unknown_like,short_fragment,True,True
14,5,reference_content,"[1] M. Sha'ban, Histology and biomechanics of cartilage, in: Cartilage Tissue and Knee Joint Biomechanics, Elsevier, 2024, pp. 25–35.","[90, 379, 855, 395]",reference_item,0.85,"[""reference content label: [1] M. Sha'ban, Histology and biomechanics of cartilage, in:""]",reference_item,0.85,reference_zone,reference_like,reference_numeric_bracket,True,True
14,6,reference_content,"[2] A.J.S. Fox, A. Bedi, S.A. Rodeo, The basic science of articular cartilage: structure, composition, and function, Sports Health 1 (6) (2009) 461–468, https://doi.org/10.1177/1941738109350438.","[90, 396, 1013, 429]",reference_item,0.85,"[""reference content label: [2] A.J.S. Fox, A. Bedi, S.A. Rodeo, The basic science of ar""]",reference_item,0.85,reference_zone,reference_like,reference_numeric_bracket,True,True
14,7,reference_content,"[3] A.R. Armiento, M. Alini, M.J. Stoddart, Articular fibrocartilage - why does hyaline cartilage fail to repair?, Adv. Drug Deliv. Rev. 146 (2019) 289–305, https://doi.org/10.1016/j.addr.2018.12.015.","[90, 431, 1012, 463]",reference_item,0.85,"[""reference content label: [3] A.R. Armiento, M. Alini, M.J. Stoddart, Articular fibroc""]",reference_item,0.85,reference_zone,reference_like,reference_numeric_bracket,True,True
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14,33,number,14,"[535, 1404, 554, 1418]",noise,0.9,"[""page number label""]",noise,0.9,tail_nonref_hold_zone,unknown_like,short_fragment,False,False
15,0,header,"J. Zimmermann, A.R. Farooqi and U. van Rienen","[74, 67, 344, 83]",noise,0.9,"[""header label""]",noise,0.9,,unknown_like,none,False,False
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16,1,header,Heliyon 10 (2024) e38112,"[864, 67, 1016, 83]",noise,0.9,"[""header label""]",noise,0.9,,unknown_like,none,False,False
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8 1 6 text Review article [75, 287, 202, 309] non_body_insert 0.3 ["short text, uncertain role"] unknown_structural 0.3 frontmatter_main_zone support_like short_fragment False False
9 1 7 doc_title Electrical stimulation for cartilage tissue engineering - A critical review from an engineer’s perspective [73, 324, 844, 389] paper_title 0.6 ["page-1 frontmatter title guard: Electrical stimulation for cartilage tissue engineering - A "] paper_title 0.6 frontmatter_main_zone support_like none True True
10 1 8 text Julius Zimmermann $ ^{a} $, Abdul Razzaq Farooqi $ ^{a,b,*} $, Ursula van Rienen $ ^{a,c,d} $ [73, 401, 752, 428] authors 0.8 ["page-1 zone author_zone: Julius Zimmermann $ ^{a} $, Abdul Razzaq Farooqi $ ^{a,b,*} "] authors 0.8 frontmatter_main_zone support_like none True True
11 1 9 text $ ^{a} $ Institute of General Electrical Engineering, University of Rostock, 18051 Rostock, Germany [73, 434, 577, 451] affiliation 0.8 ["page-1 zone affiliation_zone: $ ^{a} $ Institute of General Electrical Engineering, Univer"] affiliation 0.8 frontmatter_main_zone support_like affiliation_marker True True
12 1 10 text $ ^{b} $ Department of Electronic Engineering, Faculty of Engineering, The Islamia University of Bahawalpur, 63100 Bahawalpur, Pakistan [73, 451, 788, 469] affiliation 0.8 ["page-1 zone affiliation_zone: $ ^{b} $ Department of Electronic Engineering, Faculty of En"] affiliation 0.8 frontmatter_main_zone support_like affiliation_marker True True
13 1 11 text $ ^{2} $ Department of Ageing of Individuals and Society, Interdisciplinary Faculty, University of Rostock, 18051 Rostock, Germany [73, 469, 751, 485] affiliation 0.8 ["page-1 zone affiliation_zone: $ ^{2} $ Department of Ageing of Individuals and Society, In"] affiliation 0.8 frontmatter_main_zone support_like affiliation_marker True True
14 1 12 text $ ^{d} $ Department Life, Light & Matter, University of Rostock, 18051 Rostock, Germany [74, 486, 528, 503] affiliation 0.8 ["page-1 zone affiliation_zone: $ ^{d} $ Department Life, Light & Matter, University of Rost"] affiliation 0.8 frontmatter_main_zone support_like affiliation_marker True True
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16 1 14 text Keywords: Articular cartilage Cartilage tissue engineering Electrical stimulation Stimulation devices Numerical simulations Biophysical mechanisms [74, 589, 238, 711] frontmatter_noise 0.7 ["frontmatter noise text: Keywords:\nArticular cartilage\nCartilage tissue engineering\nE"] frontmatter_noise 0.7 body_zone body_like none False False
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18 1 16 abstract Cartilage has a limited intrinsic healing capacity. Hence, cartilage degradation and lesions pose a huge clinical challenge, particularly in an ageing society. Osteoarthritis impacts a significant num [389, 589, 1016, 840] abstract_body 0.85 ["abstract label from Paddle OCR"] abstract_body 0.85 body_zone body_like none True True
19 1 17 paragraph_title 1. Introduction [75, 943, 200, 963] section_heading 0.85 ["paragraph_title label with numbering: 1. Introduction"] section_heading 0.85 body_zone body_like heading_numbered True True
20 1 18 text Cartilage is found in the human body as hyaline, elastic, and fibrocartilage, each of which performs different functions [1]. Among the three cartilaginous tissues, hyaline cartilage is of great clini [72, 984, 1016, 1174] body_paragraph 0.6 ["default body_paragraph for text label"] body_paragraph 0.6 body_zone body_like none True True
21 1 19 footnote $ ^{*} $ Corresponding author at: Institute of General Electrical Engineering, Faculty of Computer Science and Electrical Engineering, University of Rostock, Albert Einstein Strasse 2, 18059, Rostock [72, 1211, 1012, 1247] footnote 0.7 ["footnote label: $ ^{*} $ Corresponding author at: Institute of General Elect"] footnote 0.7 body_zone body_like affiliation_marker True True
22 1 20 footnote E-mail addresses: abdul.farooqi@uni-rostock.de (A.R. Farooqi), ursula.van-rienen@uni-rostock.de (U. van Rienen). [96, 1247, 748, 1265] footnote 0.7 ["footnote label: E-mail addresses: abdul.farooqi@uni-rostock.de (A.R. Farooqi"] footnote 0.7 body_zone body_like none True True
23 1 21 footer https://doi.org/10.1016/j.heliyon.2024.e38112 Received 5 April 2024; Received in revised form 31 August 2024; Accepted 18 September 2024 [72, 1282, 690, 1321] noise 0.9 ["footer label"] noise 0.9 body_zone body_like none False False
24 1 22 footer Available online 23 September 2024 2405-8440/© 2024 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). [72, 1331, 1014, 1386] noise 0.9 ["footer label"] noise 0.9 body_zone body_like none False False
25 2 0 header J. Zimmermann, A.R. Farooqi and U. van Rienen [74, 67, 344, 84] noise 0.9 ["header label"] noise 0.9 frontmatter_side_zone support_like none False False
26 2 1 header Heliyon 10 (2024) e38112 [864, 67, 1016, 84] noise 0.9 ["header label"] noise 0.9 frontmatter_side_zone support_like none False False
27 2 2 image [321, 98, 768, 374] media_asset 0.85 ["media label: image"] media_asset 0.85 body_zone unknown_like empty True True
28 2 3 figure_title Fig. 1. Summary of the cartilage regeneration using the tissue engineering approach. Cells are expanded and seeded on a scaffold before implantation at the defect site. During cell cultivation in a mo [73, 392, 1012, 462] figure_caption 0.92 ["figure_title label: Fig. 1. Summary of the cartilage regeneration using the tiss"] figure_caption 0.92 display_zone legend_like figure_number True True
29 2 4 text At first glance, the engineering of cartilage tissue might appear straightforward: only one type of cell is present while there is no vascular network in cartilage. Moreover, the shape of the tissue t [72, 486, 1013, 548] body_paragraph 0.6 ["default body_paragraph for text label"] body_paragraph 0.6 body_zone body_like none True True
30 2 5 text Electrical stimulation can be applied in two possible scenarios: monolayer or two-dimensional (2D) and three-dimensional (3D). Both of these scenarios are important. The monolayer chondrocyte loses it [72, 549, 1014, 630] body_paragraph 0.6 ["default body_paragraph for text label"] body_paragraph 0.6 body_zone body_like none True True
31 2 6 text Approaches to engineer cartilage have focused on various scaffold materials, cells and biological or biophysical stimulation signals [9]. In recent years, matrix-assisted autologous chondrocyte implan [72, 632, 1016, 797] body_paragraph 0.6 ["default body_paragraph for text label"] body_paragraph 0.6 body_zone body_like none True True
32 2 7 text 3D cell culture approaches usually make use of scaffolds. Hydrogels as scaffold material resemble the native extracellular matrix $ [16] $ and have thus become a popular choice. They can be seeded wi [72, 798, 1015, 881] body_paragraph 0.6 ["default body_paragraph for text label"] body_paragraph 0.6 body_zone body_like none True True
33 2 8 text A challenge for the engineering of hyaline cartilage is that the produced tissue does often not have the desired mechanical properties but resembles fibrocartilage [6,9,20]. Thus, it is paramount to d [72, 882, 1016, 1006] body_paragraph 0.6 ["default body_paragraph for text label"] body_paragraph 0.6 body_zone body_like none True True
34 2 9 text Electromagnetic stimulation of cartilage can be divided into two approaches: pulsed electromagnetic field (PEMF) stimulation and electrical stimulation. In PEMF, an externally applied magnetic field i [71, 1006, 1015, 1129] body_paragraph 0.6 ["default body_paragraph for text label"] body_paragraph 0.6 body_zone body_like none True True
35 2 10 text Electrical stimulation has also been considered for regenerative medicine [27,28] in fields such as cardiac tissue engineering [29], wound healing [30–32], bone regeneration [33] or neural stimulation [72, 1131, 1016, 1317] body_paragraph 0.6 ["default body_paragraph for text label"] body_paragraph 0.6 body_zone body_like none True True
36 2 11 text The aim of this paper is to explain stimulation approaches for cartilage tissue engineering using electrical stimulation from an engineer's perspective. We will summarise the status quo such that read [73, 1318, 1015, 1381] body_paragraph 0.6 ["default body_paragraph for text label"] body_paragraph 0.6 body_zone body_like none True True
37 2 12 number 2 [538, 1404, 550, 1418] noise 0.9 ["page number label"] noise 0.9 body_zone body_like short_fragment False False
38 3 0 header J. Zimmermann, A.R. Farooqi and U. van Rienen [74, 67, 344, 83] noise 0.9 ["header label"] noise 0.9 body_zone body_like none False False
39 3 1 header Heliyon 10 (2024) e38112 [864, 67, 1016, 83] noise 0.9 ["header label"] noise 0.9 body_zone body_like none False False
40 3 2 text which permit a precise and reliable estimation of the stimulating electric field. Finally, we conclude with an overview of biophysical theories that can be used to develop hypotheses for the stimulati [72, 103, 1014, 166] body_paragraph 0.6 ["default body_paragraph for text label"] body_paragraph 0.6 body_zone body_like none True True
41 3 3 paragraph_title 2. The use of electric fields in cartilage engineering and repair [73, 187, 559, 208] section_heading 0.85 ["paragraph_title label with numbering: 2. The use of electric fields in cartilage engineering and r"] section_heading 0.85 body_zone reference_like reference_numeric_dot True True
42 3 4 text Electrical stimulation for cartilage tissue engineering has a long tradition dating back to the 1970s [41]. This section provides an overview of the different in vitro and in vivo studies and their re [72, 229, 1014, 313] body_paragraph 0.6 ["default body_paragraph for text label"] body_paragraph 0.6 body_zone body_like none True True
43 3 5 paragraph_title 2.1. In vitro stimulation [74, 334, 244, 353] subsection_heading 0.85 ["paragraph_title label with numbering: 2.1. In vitro stimulation"] subsection_heading 0.85 body_zone body_like heading_numbered True True
44 3 6 text Many in vitro studies have focused on the influence of capacitive coupling stimulation. Brighton's group is considered a pioneer in this research area and developed a stimulation protocol [42,43], whi [72, 376, 1015, 625] body_paragraph 0.6 ["default body_paragraph for text label"] body_paragraph 0.6 body_zone body_like none True True
45 3 7 text Another goal of electrical stimulation is to induce chondrocyte migration and alignment. For example, direct contact electrical stimulation has been investigated for galvanotactic $ [50] $ or dielect [73, 627, 1014, 690] body_paragraph 0.6 ["default body_paragraph for text label"] body_paragraph 0.6 body_zone body_like none True True
46 3 8 paragraph_title 2.2. In vivo stimulation [74, 711, 239, 730] subsection_heading 0.85 ["paragraph_title label with numbering: 2.2. In vivo stimulation"] subsection_heading 0.85 body_zone body_like heading_numbered True True
47 3 9 text Direct contact electric stimulation was applied for cartilage repair in rats [53] and rabbits [54]. Based on the initial investigations of Leppiello et al. [54], gel-covered electrodes were placed on [72, 753, 1016, 898] body_paragraph 0.6 ["default body_paragraph for text label"] body_paragraph 0.6 body_zone body_like none True True
48 3 10 text Systematic reviews of clinical studies on the application of electrical stimulation for the treatment of osteoarthritis have revealed a positive effect regarding improved function, but further relevan [72, 899, 1015, 1003] body_paragraph 0.6 ["default body_paragraph for text label"] body_paragraph 0.6 body_zone body_like none True True
49 3 11 text In short, in vitro experiments target a mechanistic understanding of electrical stimulation for cartilage tissue engineering. The goal is to generate cartilage-like tissue structures, e.g., based on h [72, 1004, 1014, 1108] body_paragraph 0.6 ["default body_paragraph for text label"] body_paragraph 0.6 body_zone body_like none True True
50 3 12 paragraph_title 3. Comparison and characterisation of stimulation devices [73, 1130, 528, 1150] section_heading 0.85 ["paragraph_title label with numbering: 3. Comparison and characterisation of stimulation devices"] section_heading 0.85 body_zone reference_like reference_numeric_dot True True
51 3 13 text Well-controlled in vitro experiments are needed to comprehensively investigate the interaction between applied stimulation and the cells [64]. The in vitro experiments should be replicable to ensure t [72, 1171, 1015, 1337] body_paragraph 0.6 ["default body_paragraph for text label"] body_paragraph 0.6 body_zone body_like none True True
52 3 14 text This section focuses on the electrical characteristics of the typical stimulation techniques in cartilage tissue engineering. In general, two types of stimulation are prevalent: direct contact and cap [72, 1338, 1014, 1381] body_paragraph 0.6 ["default body_paragraph for text label"] body_paragraph 0.6 body_zone body_like none True True
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58 4 4 chart [278, 507, 797, 659] media_asset 0.85 ["media label: chart"] media_asset 0.85 body_zone unknown_like empty True True
59 4 5 figure_title Fig. 2. Comparison of the most common waveforms used for electrical stimulation in the context of cartilage tissue engineering. All shown signals are periodic signals. The monophasic and biphasic puls [73, 678, 1012, 751] figure_caption 0.92 ["figure_title label: Fig. 2. Comparison of the most common waveforms used for ele"] figure_caption 0.92 display_zone legend_like figure_number True True
60 4 6 text of a thin, dielectric, non-conducting layer between the electrodes and the sample, while direct contact refers to the absence of the dielectric layer. A voltage is applied to the electrodes, resulting [72, 793, 1016, 1006] body_paragraph 0.6 ["default body_paragraph for text label"] body_paragraph 0.6 body_zone body_like none True True
61 4 7 paragraph_title 3.1. Physiological electric fields and current densities [73, 1031, 436, 1052] subsection_heading 0.85 ["paragraph_title label with numbering: 3.1. Physiological electric fields and current densities"] subsection_heading 0.85 body_zone body_like heading_numbered True True
62 4 8 text To give the reader a perspective, we will first briefly outline the physiologically expected electric fields and current densities. A first estimate of the physiologically expected electric field stre [71, 1080, 1017, 1335] body_paragraph 0.6 ["default body_paragraph for text label"] body_paragraph 0.6 body_zone body_like none True True
63 4 9 display_formula $$ E=\frac{J}{\sigma}=\frac{1\ m A\ cm^{-2}}{1\ S m^{-1}}=\frac{10\ A m^{-2}}{1\ S m^{-1}}=10\ V m^{-1}\ . $$ [121, 1343, 455, 1388] unknown_structural 0.2 ["unrecognized label 'display_formula'"] unknown_structural 0.2 body_zone body_like none False True
64 4 10 formula_number (1) [988, 1357, 1012, 1376] unknown_structural 0.2 ["unrecognized label 'formula_number'"] unknown_structural 0.2 body_zone body_like short_fragment False True
65 4 11 number 4 [538, 1404, 550, 1418] noise 0.9 ["page number label"] noise 0.9 body_zone body_like short_fragment False False
66 5 0 header J. Zimmermann, A.R. Farooqi and U. van Rienen [74, 67, 344, 83] noise 0.9 ["header label"] noise 0.9 body_zone body_like none False False
67 5 1 header Heliyon 10 (2024) e38112 [864, 67, 1016, 83] noise 0.9 ["header label"] noise 0.9 body_zone body_like none False False
68 5 2 text This exemplary calculation based on the literature data shows that the electric field amplitude that comes closest to the presumed physiological electric field amplitude should be in the order of $ 1 [72, 103, 1014, 188] body_paragraph 0.6 ["default body_paragraph for text label"] body_paragraph 0.6 body_zone body_like none True True
69 5 3 paragraph_title 3.2. Overview of direct contact stimulation approaches [73, 209, 449, 228] subsection_heading 0.85 ["paragraph_title label with numbering: 3.2. Overview of direct contact stimulation approaches"] subsection_heading 0.85 body_zone body_like heading_numbered True True
70 5 4 text Direct contact electrical stimulation is the most comprehensible approach as the electrodes are brought in direct contact with the sample and a stimulus is applied (see also the scheme in Fig. 3A). [73, 251, 1014, 291] body_paragraph 0.6 ["default body_paragraph for text label"] body_paragraph 0.6 body_zone body_like none True True
71 5 5 text Among the waveforms under consideration, rectangular pulses have been the subject of in vivo studies. For example, a current-controlled 20 $ \mu $A biphasic rectangular pulse of width 10 ms having 0. [71, 293, 1016, 647] body_paragraph 0.6 ["default body_paragraph for text label"] body_paragraph 0.6 body_zone body_like none True True
72 5 6 text Another type of stimulation using rectangular signals is nano-second pulsed electrical field (nsPEF) stimulation. High voltage electric signals leading to field strengths in the range of kV cm $ ^{-1} [72, 649, 1016, 815] body_paragraph 0.6 ["default body_paragraph for text label"] body_paragraph 0.6 body_zone body_like none True True
73 5 7 text Aside from rectangular pulses, sinusoidal waves of low frequency have been considered for direct contact stimulation. e.g., 1 kHz for the in vitro stimulation of chondrocytes [85] while 20 Hz are comm [72, 817, 1017, 1192] body_paragraph 0.6 ["default body_paragraph for text label"] body_paragraph 0.6 body_zone body_like none True True
74 5 8 text Experimental approaches in tissue engineering also exist that use DC signals [36]. The direct current is mainly the result of faradaic electrochemical reactions [97]. Agar-salt bridges are usually emp [72, 1192, 1017, 1381] body_paragraph 0.6 ["default body_paragraph for text label"] body_paragraph 0.6 body_zone body_like none True True
75 5 9 number 5 [538, 1404, 550, 1418] noise 0.9 ["page number label"] noise 0.9 body_zone body_like short_fragment False False
76 6 0 header J. Zimmermann, A.R. Farooqi and U. van Rienen [74, 67, 344, 83] noise 0.9 ["header label"] noise 0.9 body_zone body_like none False False
77 6 1 header Heliyon 10 (2024) e38112 [864, 67, 1016, 83] noise 0.9 ["header label"] noise 0.9 body_zone body_like none False False
78 6 2 text voltage but no active stimulator was used, i.e., the stimulation was passive. In contrast, DC stimulation of chondrocytes in 3D agarose culture has not led to a significant response [100]. [73, 103, 1013, 144] body_paragraph 0.6 ["default body_paragraph for text label"] body_paragraph 0.6 body_zone body_like none True True
79 6 3 text All the aforementioned in vitro and in vivo studies of direct contact electrical stimulation are summarised in Table 1. [96, 146, 933, 165] body_paragraph 0.6 ["default body_paragraph for text label"] body_paragraph 0.6 body_zone body_like none True True
80 6 4 paragraph_title 3.3. Capacitive coupling stimulation [74, 187, 324, 208] subsection_heading 0.85 ["paragraph_title label with numbering: 3.3. Capacitive coupling stimulation"] subsection_heading 0.85 body_zone body_like heading_numbered True True
81 6 5 text At first, capacitive coupling seems conflicting because no direct current flows between the sample and stimulation electrodes as the current is blocked due to an insulating layer on the stimulation el [72, 230, 1014, 353] body_paragraph 0.6 ["default body_paragraph for text label"] body_paragraph 0.6 body_zone body_like none True True
82 6 6 text For cartilage tissue engineering and regeneration involving capacitive coupling, most of the work has been performed by Brighton's group since the 1980's [42,44,45,103,110–112]. Other researchers have [73, 355, 1015, 457] body_paragraph 0.6 ["default body_paragraph for text label"] body_paragraph 0.6 body_zone body_like none True True
83 6 7 text In 2004, a study was reported by Brighton's group using 30 kHz and 120 kHz frequencies for capacitive stimulation [44]. The bovine articular chondrocytes were utilized to investigate the effect of ele [72, 460, 1016, 626] body_paragraph 0.6 ["default body_paragraph for text label"] body_paragraph 0.6 body_zone body_like none True True
84 6 8 text in recent years, other frequencies such as 1 kHz [15,47,114] or 4150 kHz [115] have been considered. Among these studies, a novel concept of coupling the fields not via a plastic layer but by niobium [72, 627, 1016, 752] body_paragraph 0.6 ["default body_paragraph for text label"] body_paragraph 0.6 body_zone body_like none True True
85 6 9 text To our knowledge, only one study uses capacitive electric stimulation of human cells describing an experimental characterisation approach $ [117] $. The authors have proposed an equivalent circuit, r [72, 753, 1014, 877] body_paragraph 0.6 ["default body_paragraph for text label"] body_paragraph 0.6 body_zone body_like none True True
86 6 10 text All studies of capacitive electrical stimulation are summarised in Table 2. [97, 878, 624, 897] body_paragraph 0.6 ["default body_paragraph for text label"] body_paragraph 0.6 body_zone body_like none True True
87 6 11 paragraph_title 3.4. Electrochemical analysis [74, 920, 278, 940] subsection_heading 0.85 ["paragraph_title label with numbering: 3.4. Electrochemical analysis"] subsection_heading 0.85 body_zone body_like heading_numbered True True
88 6 12 text In the following, all necessary techniques to estimate the electric field strength of the stimulation reliably will be compiled. Firstly, the focus is on direct contact stimulation. Please note that d [71, 962, 1015, 1170] body_paragraph 0.6 ["default body_paragraph for text label"] body_paragraph 0.6 body_zone body_like none True True
89 6 13 text The impedance is impacted by multiple parameters. For example, a suitable electrode material can reduce unwanted electrochemical reactions. Unfortunately, it is not possible to reliably predict electr [72, 1171, 1017, 1381] body_paragraph 0.6 ["default body_paragraph for text label"] body_paragraph 0.6 body_zone body_like none True True
90 6 14 number 6 [538, 1405, 550, 1418] noise 0.9 ["page number label"] noise 0.9 body_zone body_like short_fragment False False
91 7 0 figure_title Electrical stimulation studies of articular cartilage using direct coupling. Please note that consistent units were used (either kV m $ ^{-1} $ or mV cm $ ^{-1} $) to make the studies comparable. [106, 89, 1121, 112] figure_caption_candidate 0.85 ["figure_title label: Electrical stimulation studies of articular cartilage using "] figure_caption 0.85 body_zone legend_like none False False
92 7 1 table <table><tr><td>Study</td><td>Year</td><td>Stimulation protocol</td><td>Electric Field Strength</td><td>Voltage</td><td>Frequency</td><td>Current / Current density</td><td>Waveform</td><td>Tissue type [94, 100, 1396, 987] media_asset 0.85 ["media label: table"] media_asset 0.85 body_zone body_like none True True
93 7 2 vision_footnote * MSCs, mesenchymal stem cells; ADSCs, adipose-derived stem cells; BMMSCs, bone marrow-derived stromal cells; DC, direct current; NR, not reported; NA, not applicable; PW, pulse width; IG, interphase [106, 997, 1301, 1017] footnote 0.7 ["vision_footnote label: * MSCs, mesenchymal stem cells; ADSCs, adipose-derived stem "] footnote 0.7 body_zone body_like none True True
94 7 3 aside_text J. Zimmermann, A.R. Farooqi and U. van Rienen [1402, 76, 1419, 342] unknown_structural 0.2 ["unrecognized label 'aside_text'"] unknown_structural 0.2 body_zone unknown_like none False True
95 7 4 aside_text Heliyon 10 (2024) e38112 [1401, 863, 1421, 1016] unknown_structural 0.2 ["unrecognized label 'aside_text'"] unknown_structural 0.2 body_zone unknown_like none False True
96 8 0 figure_title Table 2 Electrical stimulation studies of articular cartilage using capacitive coupling. [107, 204, 548, 240] table_caption 0.9 ["table prefix matched: Table 2\nElectrical stimulation studies of articular cartilag"] table_caption 0.9 display_zone table_caption_like table_number True True
97 8 1 table <table><tr><td>Study</td><td>Year</td><td>Stimulation protocol</td><td>Electric field Strength</td><td>Voltage</td><td>Frequency</td><td>Current / Current density</td><td>Waveform</td><td>Tissue type [106, 234, 1384, 867] media_asset 0.85 ["media label: table"] media_asset 0.85 body_zone body_like none True True
98 8 2 number ∞ [66, 538, 83, 552] noise 0.9 ["page number label"] noise 0.9 body_zone unknown_like short_fragment False False
99 8 3 vision_footnote MSCs, mesenchymal stem cells; ADSCs, adipose-derived stem cells; DC, direct current; NR, not reported; PW, pulse width; CPW, counterpulse width. [107, 869, 966, 889] footnote 0.7 ["vision_footnote label: MSCs, mesenchymal stem cells; ADSCs, adipose-derived stem ce"] footnote 0.7 body_zone body_like none True True
100 8 4 aside_text J. Zimmermann, A.R. Farooqi and U. van Rienen [1400, 73, 1419, 342] unknown_structural 0.2 ["unrecognized label 'aside_text'"] unknown_structural 0.2 body_zone unknown_like none False True
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102 9 0 header J. Zimmermann, A.R. Farooqi and U. van Rienen [74, 67, 344, 83] noise 0.9 ["header label"] noise 0.9 body_zone body_like none False False
103 9 1 header Heliyon 10 (2024) e38112 [864, 67, 1016, 84] noise 0.9 ["header label"] noise 0.9 body_zone body_like none False False
104 9 2 image [166, 99, 547, 382] media_asset 0.85 ["media label: image"] media_asset 0.85 body_zone unknown_like empty True True
105 9 3 figure_title B [584, 105, 610, 135] figure_inner_text 0.9 ["panel label / figure inner text: B"] figure_inner_text 0.9 display_zone legend_like panel_label True True
106 9 4 image [583, 140, 922, 370] media_asset 0.85 ["media label: image"] media_asset 0.85 body_zone unknown_like empty True True
107 9 5 figure_title Fig. 3. Summary of electrical stimulation techniques and associated equivalent circuit models. We report the range of estimated stimulation field strengths, stimulation amplitudes, frequencies and wav [73, 403, 1015, 543] figure_caption 0.92 ["figure_title label: Fig. 3. Summary of electrical stimulation techniques and ass"] figure_caption 0.92 display_zone legend_like figure_number True True
108 9 6 text be used as an indicator. As an example of this, the transition from capacitive ( $ -80^\circ $) to diffusive ( $ -45^\circ $) characteristics was noticed for a platinum electrode in saline solution th [72, 569, 1013, 609] body_paragraph 0.6 ["default body_paragraph for text label"] body_paragraph 0.6 body_zone body_like none True True
109 9 7 text The measured impedance is described by equivalent circuit models, usually consisting of linear elements only. For example, a constant-phase element is used to describe the electrode-tissue interface. [72, 611, 1014, 818] body_paragraph 0.6 ["default body_paragraph for text label"] body_paragraph 0.6 body_zone body_like none True True
110 9 8 text The electrical stimulation approaches can be linked to electrochemical characterisation methods. Direct current stimulation at a fixed voltage results in the generation of a time-dependent current. In [72, 819, 1015, 943] body_paragraph 0.6 ["default body_paragraph for text label"] body_paragraph 0.6 body_zone body_like none True True
111 9 9 text Alternating current stimulation using sine or periodic waves resembles measuring the impedance if both voltage and current are recorded. Probing the impedance over an extensive frequency range is call [71, 945, 1016, 1131] body_paragraph 0.6 ["default body_paragraph for text label"] body_paragraph 0.6 body_zone body_like none True True
112 9 10 text In short, a general expected equivalent circuit can be formulated for each stimulation type. This permits the establishment of a corresponding electrochemical measurement approach for each stimulation [73, 1132, 1014, 1195] body_paragraph 0.6 ["default body_paragraph for text label"] body_paragraph 0.6 body_zone body_like none True True
113 9 11 paragraph_title 3.5. Towards reliable and controllable electrical stimulation [73, 1214, 483, 1235] subsection_heading 0.85 ["paragraph_title label with numbering: 3.5. Towards reliable and controllable electrical stimulatio"] subsection_heading 0.85 body_zone body_like heading_numbered True True
114 9 12 text The comparison of the existing stimulation approaches does not permit to identify a clear formulation of the ‘ideal’ electrical stimulation of cartilage (cf. Tables 1 and 2) despite reports of benefic [72, 1255, 1016, 1381] body_paragraph 0.6 ["default body_paragraph for text label"] body_paragraph 0.6 body_zone body_like none True True
115 9 13 number 9 [538, 1404, 550, 1418] noise 0.9 ["page number label"] noise 0.9 body_zone body_like short_fragment False False
116 10 0 header J. Zimmermann, A.R. Farooqi and U. van Rienen [74, 67, 344, 83] noise 0.9 ["header label"] noise 0.9 body_zone body_like none False False
117 10 1 header Heliyon 10 (2024) e38112 [864, 67, 1016, 83] noise 0.9 ["header label"] noise 0.9 body_zone body_like none False False
118 10 2 text A key ingredient is the impedance spectroscopy of the stimulation system. Ideally, it is conducted with a sample having known properties (e.g., an electrolyte solution) and on the actual sample multip [72, 104, 1016, 333] body_paragraph 0.6 ["default body_paragraph for text label"] body_paragraph 0.6 body_zone body_like none True True
119 10 3 text An open question is how to numerically model the electrode-tissue interface layer. One possibility is to assume a voltage divider, in which the interface impedance is described as a lumped element [86 [71, 335, 1016, 647] body_paragraph 0.6 ["default body_paragraph for text label"] body_paragraph 0.6 body_zone body_like none True True
120 10 4 text In our opinion, the lack of current and voltage recording data (compare Tables 1 and 2) currently marks the biggest problem in the understanding and retrospectively analysing of the electrical stimula [71, 648, 1017, 1045] body_paragraph 0.6 ["default body_paragraph for text label"] body_paragraph 0.6 body_zone body_like none True True
121 10 5 text The cellular dielectric properties cover a wide range [144], and their influence on the modelling result needs to be carefully tested [109]. In future research, optical methods combined with impedance [71, 1046, 1015, 1150] body_paragraph 0.6 ["default body_paragraph for text label"] body_paragraph 0.6 body_zone body_like none True True
122 10 6 text With this information, more advanced biophysical models can be developed. In the following section, some potential biophysical interactions are introduced. In addition to the biophysical modelling, sa [72, 1151, 1014, 1234] body_paragraph 0.6 ["default body_paragraph for text label"] body_paragraph 0.6 body_zone body_like none True True
123 10 7 paragraph_title 4. Biophysical theories of the interaction of electric fields and cells [73, 1254, 594, 1274] section_heading 0.85 ["paragraph_title label with numbering: 4. Biophysical theories of the interaction of electric field"] section_heading 0.85 body_zone reference_like reference_numeric_dot True True
124 10 8 text Various studies have hypothesised that electric fields can stimulate non-excitable cells, e.g. chondrocytes, to differentiate, proliferate or produce chondrogenic proteins. Still, it is unclear which [72, 1297, 1017, 1381] body_paragraph 0.6 ["default body_paragraph for text label"] body_paragraph 0.6 body_zone body_like none True True
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131 11 5 figure_title Equivalent circuit [641, 105, 786, 126] figure_caption 0.85 ["figure_title label: Equivalent circuit"] figure_caption 0.85 body_zone unknown_like short_fragment True True
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141 11 15 figure_title Integrate cell model [446, 464, 629, 486] figure_caption_candidate 0.85 ["figure_title label: Integrate cell model"] figure_caption 0.85 body_zone unknown_like none False False
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144 11 18 image [358, 630, 719, 764] media_asset 0.85 ["media label: image"] media_asset 0.85 body_zone unknown_like empty True True
145 11 19 figure_title Fig. 4. Future vision of cartilage tissue engineering experiments: Experiments are conducted alongside numerical simulations (panel A, adapted from [72], it is under CC-BY licence (CC BY 4.0)). A nume [72, 786, 1017, 924] figure_caption 0.92 ["figure_title label: Fig. 4. Future vision of cartilage tissue engineering experi"] figure_caption 0.92 display_zone legend_like figure_number True True
146 11 20 paragraph_title 4.1. Induced transmembrane potential [75, 957, 339, 976] subsection_heading 0.85 ["paragraph_title label with numbering: 4.1. Induced transmembrane potential"] subsection_heading 0.85 body_zone body_like heading_numbered True True
147 11 21 text The cell membrane is a lipid bilayer having a thickness of around 7 nm [133], which separates the cell interior (the cytoplasm) from the extracellular medium. Each cell type has a characteristic resti [73, 1002, 1013, 1064] body_paragraph 0.6 ["default body_paragraph for text label"] body_paragraph 0.6 body_zone body_like none True True
148 11 22 text Compared to eukaryotic cells, which have a resting transmembrane potential in the range of $ -70 $ mV to $ -40 $ mV [147], chondrocytes have an increased resting potential of $ -46 $ mV to $ -11 $ [73, 1065, 1014, 1168] body_paragraph 0.6 ["default body_paragraph for text label"] body_paragraph 0.6 body_zone body_like none True True
149 11 23 text Voltage-dependent calcium channels are essential in forming and maintaining healthy cartilage [150]. However, their reaction mechanism to external electrical stimuli is unclear [21]. Electrical stimul [73, 1169, 1014, 1296] body_paragraph 0.6 ["default body_paragraph for text label"] body_paragraph 0.6 body_zone body_like none True True
150 11 24 text Xu et al. have reported that blockers of voltage-dependent calcium channels inhibit entirely the electrical stimulation effects $ [103] $. No estimate of the induced transmembrane potential in adult [72, 1297, 1013, 1381] body_paragraph 0.6 ["default body_paragraph for text label"] body_paragraph 0.6 body_zone body_like none True True
151 11 25 number 11 [534, 1404, 553, 1418] noise 0.9 ["page number label"] noise 0.9 body_zone body_like short_fragment False False
152 12 0 header J. Zimmermann, A.R. Farooqi and U. van Rienen [74, 67, 344, 83] noise 0.9 ["header label"] noise 0.9 body_zone body_like none False False
153 12 1 header Heliyon 10 (2024) e38112 [864, 67, 1016, 83] noise 0.9 ["header label"] noise 0.9 body_zone body_like none False False
154 12 2 text When even higher membrane potentials are induced (hundreds of mV), it comes to electropemeabilisation [147]. The membrane becomes porous due to a strong electric field inside the very thin membrane. T [72, 103, 1014, 165] body_paragraph 0.6 ["default body_paragraph for text label"] body_paragraph 0.6 body_zone body_like none True True
155 12 3 text One can estimate the lower effective range of the induced transmembrane potential by comparing the drift current to the random diffusion current. The applied electric field causes the drift current, w [72, 167, 1014, 293] body_paragraph 0.6 ["default body_paragraph for text label"] body_paragraph 0.6 body_zone body_like none True True
156 12 4 text To summarize, the aforementioned observations indicate that very large induced transmembrane potentials greater than 100 mV should be avoided in cartilage tissue engineering to not destroy the membran [72, 294, 1014, 417] body_paragraph 0.6 ["default body_paragraph for text label"] body_paragraph 0.6 body_zone body_like none True True
157 12 5 text There exist analytical [157–159] as well as numerical techniques [160] to estimate the transmembrane potential [109,145]. An experimental validation of the estimated values has been presented in [160, [72, 420, 1016, 610] body_paragraph 0.6 ["default body_paragraph for text label"] body_paragraph 0.6 body_zone body_like none True True
158 12 6 paragraph_title 4.2. Electromechanical interaction [74, 634, 313, 653] subsection_heading 0.85 ["paragraph_title label with numbering: 4.2. Electromechanical interaction"] subsection_heading 0.85 body_zone body_like heading_numbered True True
159 12 7 text The electromechanical interaction of electrical stimulation can occur at the cellular scale, the membrane scale, or even the protein scale. [72, 679, 1014, 719] body_paragraph 0.6 ["default body_paragraph for text label"] body_paragraph 0.6 body_zone body_like none True True
160 12 8 paragraph_title 4.2.1. Cell migration: dielectrophoresis and galvanotaxis [72, 745, 461, 764] subsection_heading 0.85 ["paragraph_title label with numbering: 4.2.1. Cell migration: dielectrophoresis and galvanotaxis"] subsection_heading 0.85 body_zone body_like heading_numbered True True
161 12 9 text Cell migration due to an external electric field is referred to as electrotaxis or galvanotaxis (direct current fields) and dielectrophoresis (alternating current fields) [162–164]. Moreover, cells ma [72, 766, 1016, 849] body_paragraph 0.6 ["default body_paragraph for text label"] body_paragraph 0.6 body_zone body_like none True True
162 12 10 text In electrotaxis, the electric field functions as a compass for the cells [162]. The electric field does not control the cells in a defined direction compared to electrophoresis. Instead, electrotaxis [72, 851, 1016, 1018] body_paragraph 0.6 ["default body_paragraph for text label"] body_paragraph 0.6 body_zone body_like none True True
163 12 11 text Cells can also migrate due to dielectrophoresis upon alternating current electrical stimulation. In contrast to galvanotaxis, dielectrophoresis is explained by classical electrodynamics [163]. The for [72, 1020, 1016, 1188] body_paragraph 0.6 ["default body_paragraph for text label"] body_paragraph 0.6 body_zone body_like none True True
164 12 12 paragraph_title 4.2.2. Electro-deformation [74, 1211, 262, 1230] subsection_heading 0.85 ["paragraph_title label with numbering: 4.2.2. Electro-deformation"] subsection_heading 0.85 body_zone body_like heading_numbered True True
165 12 13 text Even though homogeneous electric fields can deform the spherical cells, they cannot induce migration of these cells [166,167]. It has been reported that MSCs embedded in hydrogels stimulated by the ca [72, 1232, 1015, 1381] body_paragraph 0.6 ["default body_paragraph for text label"] body_paragraph 0.6 body_zone body_like none True True
166 12 14 number 12 [535, 1404, 554, 1418] noise 0.9 ["page number label"] noise 0.9 body_zone body_like short_fragment False False
167 13 0 header J. Zimmermann, A.R. Farooqi and U. van Rienen [74, 67, 344, 83] noise 0.9 ["header label"] noise 0.9 body_zone body_like none False False
168 13 1 header Heliyon 10 (2024) e38112 [864, 66, 1016, 83] noise 0.9 ["header label"] noise 0.9 body_zone body_like none False False
169 13 2 text To explain the electro-deformation phenomenon of cells, giant vesicles (cell-like constructs usually having no nucleus) have been studied $ [169] $. The morphological changes in alternating current f [72, 103, 1014, 252] body_paragraph 0.6 ["default body_paragraph for text label"] body_paragraph 0.6 body_zone body_like none True True
170 13 3 paragraph_title 4.2.3. Interaction with membrane constituents [73, 275, 393, 293] subsection_heading 0.85 ["paragraph_title label with numbering: 4.2.3. Interaction with membrane constituents"] subsection_heading 0.85 body_zone body_like heading_numbered True True
171 13 4 text The effect of oscillating electric fields could be related to electroconformational coupling, which has been explored since the late 1980s [175]. However, the effect has not been visually observed but [72, 295, 1013, 421] body_paragraph 0.6 ["default body_paragraph for text label"] body_paragraph 0.6 body_zone body_like none True True
172 13 5 text The biological effect of the electroconformational coupling has been linked to H⁺-ATPase membrane pumps [176], also affected by the membrane potential [182]. Sodium potassium pumps (Na⁺/K⁺-ATPase) are [73, 422, 1013, 485] body_paragraph 0.6 ["default body_paragraph for text label"] body_paragraph 0.6 body_zone body_like none True True
173 13 6 text Another open research topic is the motility of membrane constituents. For example, lipid rafts have been shown to cluster upon electrical stimulation in fibroblasts [185]. At extremely low frequencies [72, 486, 1015, 589] body_paragraph 0.6 ["default body_paragraph for text label"] body_paragraph 0.6 body_zone body_like none True True
174 13 7 text Moreover, chondrocytes also express Piezo channels [188,189] or TRPV4 [190] mechanosensitive membrane channels. The mechanism of interaction of the membrane and its constituents with electrical stimul [71, 590, 1017, 803] body_paragraph 0.6 ["default body_paragraph for text label"] body_paragraph 0.6 body_zone body_like none True True
175 13 8 paragraph_title 5. Summary and outlook [74, 824, 272, 844] section_heading 0.85 ["paragraph_title label with numbering: 5. Summary and outlook"] section_heading 0.85 body_zone reference_like reference_numeric_dot True True
176 13 9 text We discussed electrical stimulation as a biophysical intervention for the repair and regeneration of cartilage. Despite decades of research, it appears that no reliable electrical stimulation protocol [71, 869, 1016, 1080] body_paragraph 0.6 ["default body_paragraph for text label"] body_paragraph 0.6 body_zone body_like none True True
177 13 10 text We believe that improved reporting standards focusing on technical replicability combined with continuous monitoring of stimulation parameters will eventually enable a more effective determination of [71, 1080, 1016, 1228] body_paragraph 0.6 ["default body_paragraph for text label"] body_paragraph 0.6 body_zone body_like none True True
178 13 11 paragraph_title CRediT authorship contribution statement [74, 1251, 404, 1272] backmatter_heading 0.8 ["backmatter heading on page 13: CRediT authorship contribution statement"] backmatter_heading_candidate 0.8 body_zone body_like none True True
179 13 12 text Julius Zimmermann: Writing – review & editing, Writing – original draft, Visualization, Validation, Software, Resources, Methodology, Investigation, Formal analysis, Data curation, Conceptualization. [72, 1296, 1014, 1381] backmatter_body 0.6 ["default body_paragraph for text label"] body_paragraph 0.6 body_zone body_like none True True
180 13 13 number 13 [535, 1404, 554, 1418] noise 0.9 ["page number label"] noise 0.9 body_zone body_like short_fragment False False
181 14 0 header J. Zimmermann, A.R. Farooqi and U. van Rienen [75, 67, 344, 84] noise 0.9 ["header label"] noise 0.9 body_zone body_like none False False
182 14 1 header Heliyon 10 (2024) e38112 [864, 66, 1016, 84] noise 0.9 ["header label"] noise 0.9 body_zone body_like none False False
183 14 2 text The authors declare that they have no known competing financial interests or personal relationships that could have appeared to uence the work reported in this paper. [102, 146, 1020, 188] body_paragraph 0.6 ["default body_paragraph for text label"] body_paragraph 0.6 body_zone body_like none True True
184 14 3 text The authors are grateful to Rainer Bader for pointing out the need for research into the appropriate modelling of electrical stimulation of cartilage tissue. We thank Simone Krüger and Nils Arbeiter f [72, 252, 1014, 316] backmatter_body 0.6 ["default body_paragraph for text label", "promoted in tail spread from body_paragraph"] body_paragraph 0.6 body_zone body_like none True True
185 14 4 paragraph_title References [74, 337, 164, 357] reference_heading 0.9 ["references heading: References"] reference_heading 0.9 reference_zone unknown_like short_fragment True True
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2,1,doc_title,Re-tear after arthroscopic rotator cuff tear surgery: risk analysis using machine learning,"[150, 145, 1071, 175]",paper_title,0.8,"[""page-1 zone title_zone: Re-tear after arthroscopic rotator cuff tear surgery: risk a""]",paper_title,0.8,frontmatter_main_zone,support_like,none,True,True
2,2,text,"Issei Shinohara, M.D., Ph.D. $ ^{1} $, Yutaka Mifune $ ^{*} $, M.D., Ph.D. $ ^{1} $, Atsuyuki Inui, M.D., Ph.D. $ ^{1} $, Hanako Nishimoto, M.D., Ph.D. $ ^{1} $, Tomoya Yoshikawa, M.D., Ph.D. $ ^{1} $","[137, 259, 1069, 456]",authors,0.8,"[""page-1 zone author_zone: Issei Shinohara, M.D., Ph.D. $ ^{1} $, Yutaka Mifune $ ^{*} ""]",authors,0.8,frontmatter_main_zone,support_like,none,True,True
2,3,text,"1. Department of Orthopaedic Surgery, Kobe University Graduate School of Medicine, 5-2, Kusunoki-cho7, Chuo-ku, Kobe, Hyogo 650-0017, Japan","[140, 482, 1041, 556]",affiliation,0.8,"[""page-1 zone affiliation_zone: 1. Department of Orthopaedic Surgery, Kobe University Gradua""]",affiliation,0.8,body_zone,reference_like,reference_numeric_dot,True,True
2,4,text,"2. Department of Orthopaedic Surgery, Himeji St Mary's Hospital, Himeji, Hyogo, Japan
**These authors contributed equally to this work.**","[138, 581, 1017, 653]",frontmatter_support,0.78,"[""first-surviving-page support text: 2. Department of Orthopaedic Surgery, Himeji St Mary's Hospi""]",frontmatter_support,0.78,body_zone,reference_like,reference_numeric_dot,True,True
2,5,text,"*Correspondence to: Yutaka Mifune, MD, PhD, Department of Orthopaedic Surgery, Kobe University Graduate School of Medicine, 7-5-1 Kusunoki-cho, Chuo-ku, Kobe, 650-0017 Hyogo, Japan; Phone: +81-78-382-","[138, 680, 1089, 796]",frontmatter_noise,0.8,"[""page-1 zone journal_furniture_zone: *Correspondence to: Yutaka Mifune, MD, PhD, Department of Or""]",frontmatter_noise,0.8,frontmatter_main_zone,support_like,none,False,False
2,6,text,The Kobe University Graduate School of Medicine Ethics Committee approved this study (no. 1735).,"[139, 878, 1056, 948]",affiliation,0.8,"[""page-1 zone affiliation_zone: The Kobe University Graduate School of Medicine Ethics Commi""]",affiliation,0.8,frontmatter_main_zone,support_like,none,True,True
2,7,text,Running-head title: Machine learning predicts rotator cuff re-tear,"[138, 977, 775, 1007]",body_paragraph,0.6,"[""default body_paragraph for text label""]",body_paragraph,0.6,frontmatter_main_zone,support_like,none,True,True
2,8,paragraph_title,Disclaimers:,"[139, 1035, 276, 1063]",sub_subsection_heading,0.6,"[""unnumbered paragraph_title, inferred level sub_subsection_heading: Disclaimers:""]",sub_subsection_heading,0.6,frontmatter_main_zone,heading_like,short_fragment,True,True
2,9,text,Funding: No funding was disclosed by the authors.,"[137, 1092, 636, 1122]",body_paragraph,0.6,"[""default body_paragraph for text label""]",body_paragraph,0.6,frontmatter_main_zone,support_like,none,True,True
2,10,text,"Conflicts of interest: The authors, their immediate families, and any research foundation with which they are affiliated have not received any financial payments or other benefits from any commercial ","[137, 1149, 1044, 1262]",body_paragraph,0.6,"[""default body_paragraph for text label""]",body_paragraph,0.6,body_zone,body_like,none,True,True
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3,1,header,Journal Pre-proof,"[480, 60, 678, 88]",frontmatter_noise,0.98,"[""journal pre-proof running header suppressed: p3 y=60/1684""]",frontmatter_noise,0.98,preproof_cover_zone,unknown_like,preproof_marker,False,False
3,2,abstract,Re-tear after arthroscopic rotator cuff tear surgery: risk analysis using machine learning,"[168, 143, 1019, 177]",abstract_body,0.85,"[""abstract label from Paddle OCR""]",abstract_body,0.85,body_zone,body_like,none,True,True
3,3,paragraph_title,Abstract,"[141, 225, 241, 257]",abstract_heading,0.95,"[""abstract heading""]",abstract_heading,0.95,body_zone,heading_like,short_fragment,True,True
3,4,abstract,"Background: Postoperative rotator cuff re-tear after arthroscopic rotator cuff repair (ARCR) is still a major problem. Various risk factors such as age, gender, and tear size have been reported. Recen","[136, 269, 1053, 1533]",abstract_body,0.85,"[""abstract label from Paddle OCR""]",abstract_body,0.85,body_zone,body_like,none,True,True
3,5,number,1,"[588, 1548, 602, 1570]",noise,0.9,"[""page number label""]",noise,0.9,body_zone,body_like,short_fragment,False,False
4,0,aside_text,"28
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4,1,header,Journal Pre-proof,"[480, 61, 677, 88]",frontmatter_noise,0.98,"[""journal pre-proof running header suppressed: p4 y=61/1684""]",frontmatter_noise,0.98,preproof_cover_zone,unknown_like,preproof_marker,False,False
4,2,text,"Conclusions: The ML classifier model predicted re-tears after ARCR with high accuracy, and the AI model showed that the most important characteristics affecting re-tears were age and imaging findings,","[133, 147, 1049, 561]",body_paragraph,0.6,"[""default body_paragraph for text label""]",body_paragraph,0.6,body_zone,body_like,none,True,True
4,3,text,Postoperative re-tear is still a problem in arthroscopic rotator cuff repair (ARCR) for degenerative rotator cuff tears (RCTs). The reported re-tear rate after ARCR varies depending on the suture meth,"[137, 754, 1052, 1503]",body_paragraph,0.6,"[""default body_paragraph for text label""]",body_paragraph,0.6,body_zone,body_like,none,True,True
4,4,number,2,"[587, 1548, 603, 1570]",noise,0.9,"[""page number label""]",noise,0.9,body_zone,body_like,short_fragment,False,False
5,0,aside_text,"53
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5,1,header,Journal Pre-proof,"[480, 61, 676, 87]",frontmatter_noise,0.98,"[""journal pre-proof running header suppressed: p5 y=61/1684""]",frontmatter_noise,0.98,preproof_cover_zone,unknown_like,preproof_marker,False,False
5,2,text,"classification by MRI images. There are few reports taking stump classification into account that may be useful for predicting re-tears after ARCR. In this study, we focused on the analysis of clinica","[138, 149, 1051, 1118]",body_paragraph,0.6,"[""default body_paragraph for text label""]",body_paragraph,0.6,body_zone,body_like,none,True,True
5,3,paragraph_title,Materials and Methods,"[141, 1251, 386, 1278]",section_heading,0.9,"[""explicit scholarly heading: Materials and Methods""]",section_heading,0.9,body_zone,heading_like,canonical_section_name,True,True
5,4,text,Ethical approval,"[141, 1305, 311, 1334]",unknown_structural,0.3,"[""short text, uncertain role""]",unknown_structural,0.3,body_zone,body_like,short_fragment,False,True
5,5,text,"This study was approved by the appropriate review board, and informed consent was obtained from all patients involved.","[140, 1358, 1049, 1444]",body_paragraph,0.6,"[""default body_paragraph for text label""]",body_paragraph,0.6,body_zone,body_like,none,True,True
5,6,text,Data collection,"[141, 1470, 296, 1498]",unknown_structural,0.3,"[""short text, uncertain role""]",unknown_structural,0.3,body_zone,body_like,short_fragment,False,True
5,7,number,3,"[588, 1549, 603, 1570]",noise,0.9,"[""page number label""]",noise,0.9,body_zone,body_like,short_fragment,False,False
6,0,aside_text,"78
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6,1,header,Journal Pre-proof,"[480, 61, 677, 88]",frontmatter_noise,0.98,"[""journal pre-proof running header suppressed: p6 y=61/1684""]",frontmatter_noise,0.98,preproof_cover_zone,unknown_like,preproof_marker,False,False
6,2,text,"Patients who underwent ARCR for degenerative complete rotator cuff tears from April 2017 to June 2021 at our institution or affiliated institutions were included. ARCR was performed by two surgeons, Y","[138, 144, 1048, 662]",body_paragraph,0.6,"[""default body_paragraph for text label""]",body_paragraph,0.6,body_zone,body_like,none,True,True
6,3,text,"Each patient parameter is expressed as mean ± standard deviation. To compare patient backgrounds with and without re-tears, the Mann–Whitney U test was used to compare two variables (e.g., gender) and","[137, 699, 1030, 894]",body_paragraph,0.6,"[""default body_paragraph for text label""]",body_paragraph,0.6,body_zone,body_like,none,True,True
6,4,paragraph_title,Machine learning,"[141, 918, 321, 948]",subsection_heading,0.6,"[""unnumbered paragraph_title, inferred level subsection_heading: Machine learning""]",subsection_heading,0.6,body_zone,heading_like,short_fragment,True,True
6,5,text,"The data collection and ML workflow are shown in Fig. 2. Five supervised algorithms were applied to validate clinical data $ ^{20,22} $ (logistic regression, random forest, $ ^{1} $ adaptive boost (Ad","[137, 973, 1054, 1502]",body_paragraph,0.6,"[""default body_paragraph for text label""]",body_paragraph,0.6,body_zone,body_like,none,True,True
6,6,number,4,"[588, 1549, 603, 1570]",noise,0.9,"[""page number label""]",noise,0.9,body_zone,body_like,short_fragment,False,False
7,0,aside_text,"103
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7,1,header,Journal Pre-proof,"[480, 61, 677, 88]",frontmatter_noise,0.98,"[""journal pre-proof running header suppressed: p7 y=61/1684""]",frontmatter_noise,0.98,preproof_cover_zone,unknown_like,preproof_marker,False,False
7,2,text,"method that attempts to obtain better predictions by using multiple training models and performing majority voting on the results. $ ^{1} $ On the other hand, Adaboost, Catboost, and LightGBM are grad","[135, 145, 1059, 1504]",body_paragraph,0.6,"[""default body_paragraph for text label""]",body_paragraph,0.6,body_zone,body_like,none,True,True
7,3,number,5,"[587, 1548, 603, 1570]",noise,0.9,"[""page number label""]",noise,0.9,body_zone,body_like,short_fragment,False,False
8,0,aside_text,"128
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8,1,header,Journal Pre-proof,"[481, 61, 676, 88]",frontmatter_noise,0.98,"[""journal pre-proof running header suppressed: p8 y=61/1684""]",frontmatter_noise,0.98,preproof_cover_zone,unknown_like,preproof_marker,False,False
8,2,text,"randomly shuffled; $ ^{12} $ the Shapley additive explanation (SHAP) value is defined as the contribution of each feature to the model prediction based on game theory. $ ^{40} $ Briefly, it is a metho","[137, 144, 1037, 340]",body_paragraph,0.6,"[""default body_paragraph for text label""]",body_paragraph,0.6,body_zone,body_like,none,True,True
8,3,paragraph_title,Results Study participants and statistical analysis,"[138, 422, 550, 508]",subsection_heading,0.6,"[""unnumbered paragraph_title, inferred level subsection_heading: Results Study participants and statistical analysis""]",subsection_heading,0.6,body_zone,heading_like,none,True,True
8,4,footer,,"[138, 477, 550, 508]",noise,0.9,"[""footer label""]",noise,0.9,body_zone,body_like,empty,False,False
8,5,text,"Of the 582 cases who underwent ARCR at our institution or affiliated institutions, 353 were finally included after excluding re-tears (12 cases), traumatic tears (182 cases), and patients who required","[137, 531, 1052, 837]",body_paragraph,0.6,"[""default body_paragraph for text label""]",body_paragraph,0.6,body_zone,body_like,none,True,True
8,6,paragraph_title,Prediction of rotator cuff re-tear in each ML model,"[139, 873, 641, 904]",subsection_heading,0.6,"[""unnumbered paragraph_title, inferred level subsection_heading: Prediction of rotator cuff re-tear in each ML model""]",subsection_heading,0.6,body_zone,heading_like,none,True,True
8,7,text,"Fig. 3 shows a heat map representing the correlation between each parameter and rotator cuff re-tear. Warm colors indicate a positive correlation, while cold colors indicate a negative correlation. Th","[137, 923, 1052, 1283]",body_paragraph,0.9,"[""figure caption candidate (body narrative): Fig. 3 shows a heat map representing the correlation between""]",figure_caption_candidate,0.9,display_zone,legend_like,figure_number,True,True
8,8,paragraph_title,Important features of the predictor variables,"[140, 1310, 576, 1343]",subsection_heading,0.6,"[""unnumbered paragraph_title, inferred level subsection_heading: Important features of the predictor variables""]",subsection_heading,0.6,body_zone,heading_like,none,True,True
8,9,text,"To detect the importance of each parameter for predicting postoperative rotator cuff re-tear, feature importance was calculated for the LightGBM model, which showed the highest AUC. Age, stump classif","[137, 1362, 1042, 1510]",body_paragraph,0.6,"[""default body_paragraph for text label""]",body_paragraph,0.6,body_zone,body_like,none,True,True
8,10,number,6,"[588, 1549, 603, 1571]",noise,0.9,"[""page number label""]",noise,0.9,body_zone,body_like,short_fragment,False,False
9,0,aside_text,"153
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9,1,header,Journal Pre-proof,"[480, 61, 677, 88]",frontmatter_noise,0.98,"[""journal pre-proof running header suppressed: p9 y=61/1684""]",frontmatter_noise,0.98,preproof_cover_zone,unknown_like,preproof_marker,False,False
9,2,text,"associated with postoperative rotator cuff re-tear in the LightGBM model (Fig. 5a). The SHAP score showed stump classification, tear size, and age as important characteristics. As shown in Fig. 5b, st","[138, 144, 1053, 343]",body_paragraph,0.6,"[""default body_paragraph for text label""]",body_paragraph,0.6,body_zone,body_like,none,True,True
9,3,paragraph_title,Discussion,"[140, 476, 256, 506]",section_heading,0.9,"[""explicit scholarly heading: Discussion""]",section_heading,0.9,body_zone,heading_like,canonical_section_name,True,True
9,4,text,"The ML classification models predicted re-tears after ARCR with high accuracy. Among the five used models, LightGBM showed the highest AUC. In the LightGBM model, age, stump classification, and tear s","[138, 532, 1042, 728]",body_paragraph,0.6,"[""default body_paragraph for text label""]",body_paragraph,0.6,body_zone,body_like,none,True,True
9,5,text,"In the last decades, AI techniques based on mathematical modeling have been developed; ML is one of the AI-based approaches, and ML models are increasingly integrated into clinical diagnosis and the p","[137, 753, 1054, 1504]",body_paragraph,0.6,"[""default body_paragraph for text label""]",body_paragraph,0.6,body_zone,body_like,none,True,True
9,6,number,7,"[588, 1548, 603, 1570]",noise,0.9,"[""page number label""]",noise,0.9,body_zone,body_like,short_fragment,False,False
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10,1,header,Journal Pre-proof,"[480, 61, 677, 88]",frontmatter_noise,0.98,"[""journal pre-proof running header suppressed: p10 y=61/1684""]",frontmatter_noise,0.98,preproof_cover_zone,unknown_like,preproof_marker,False,False
10,2,text,"framework that uses a decision-tree-based learning algorithm, adopting a histogram algorithm and a depth-limited leaf-wise leaf growth strategy. $ ^{41} $ This strategy increases computational efficie","[134, 153, 1054, 1499]",body_paragraph,0.6,"[""default body_paragraph for text label""]",body_paragraph,0.6,body_zone,body_like,none,True,True
10,3,number,8,"[588, 1548, 603, 1570]",noise,0.9,"[""page number label""]",noise,0.9,body_zone,body_like,short_fragment,False,False
11,0,aside_text,"203
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11,1,header,Journal Pre-proof,"[480, 61, 677, 88]",frontmatter_noise,0.98,"[""journal pre-proof running header suppressed: p11 y=61/1684""]",frontmatter_noise,0.98,preproof_cover_zone,unknown_like,preproof_marker,False,False
11,2,text,"This study has some limitations. First, although the model performed well on the present data set, the number of cases in the original data is not large. Second, we did not consider factors by procedu","[137, 146, 1055, 564]",body_paragraph,0.6,"[""default body_paragraph for text label""]",body_paragraph,0.6,body_zone,body_like,none,True,True
11,3,paragraph_title,Conclusion,"[139, 641, 264, 670]",section_heading,0.9,"[""explicit scholarly heading: Conclusion""]",section_heading,0.9,body_zone,heading_like,canonical_section_name,True,True
11,4,text,"The ML classifier model predicted re-tears after ARCR with high accuracy, and the AI model showed that the most important characteristics affecting re-tears were age and imaging findings, including st","[136, 698, 1053, 1004]",body_paragraph,0.6,"[""default body_paragraph for text label""]",body_paragraph,0.6,body_zone,body_like,none,True,True
11,5,paragraph_title,References,"[140, 1083, 260, 1112]",reference_heading,0.9,"[""references heading: References""]",reference_heading,0.9,reference_zone,heading_like,short_fragment,True,True
11,6,reference_content,"1. Alderden J, Pepper GA, Wilson A, Whitney JD, Richardson S, Butcher R, et al. Predicting pressure injury in critical care patients: A machine-learning model. Am J Crit Care 2018;27:461-468. doi: $ ","[139, 1125, 1037, 1235]",reference_item,0.85,"[""reference content label: 1. Alderden J, Pepper GA, Wilson A, Whitney JD, Richardson S""]",reference_item,0.85,reference_zone,reference_like,reference_numeric_dot,True,True
11,7,reference_content,"2. Barth J, Andrieu K, Fotiadis E, Hannink G, Barthelemy R, Saffarini M. Critical period and risk factors for retear following arthroscopic repair of the rotator cuff. Knee Surg Sports Traumatol Arthr","[139, 1250, 1040, 1361]",reference_item,0.85,"[""reference content label: 2. Barth J, Andrieu K, Fotiadis E, Hannink G, Barthelemy R, ""]",reference_item,0.85,reference_zone,reference_like,reference_numeric_dot,True,True
11,8,reference_content,"3. Cho NS, Lee BG, Rhee YG. Arthroscopic rotator cuff repair using a suture bridge technique: is the repair integrity actually maintained? Am J Sports Med 2011;39:2108-2116. doi: $ \underline{\text{1","[139, 1375, 955, 1483]",reference_item,0.85,"[""reference content label: 3. Cho NS, Lee BG, Rhee YG. Arthroscopic rotator cuff repair""]",reference_item,0.85,reference_zone,reference_like,reference_numeric_dot,True,True
11,9,number,9,"[588, 1548, 603, 1569]",noise,0.9,"[""page number label""]",noise,0.9,tail_nonref_hold_zone,unknown_like,short_fragment,False,False
12,0,aside_text,"230
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12,1,header,Journal Pre-proof,"[479, 61, 678, 88]",frontmatter_noise,0.98,"[""journal pre-proof running header suppressed: p12 y=61/1684""]",frontmatter_noise,0.98,preproof_cover_zone,unknown_like,preproof_marker,False,False
12,2,reference_content,"4. Cho NS, Rhee YG. The factors affecting the clinical outcome and integrity of arthroscopically repaired rotator cuff tears of the shoulder. Clin Orthop Surg 2009;1:96-104. doi: $ \underline{\text{1","[139, 145, 952, 256]",reference_item,0.85,"[""reference content label: 4. Cho NS, Rhee YG. The factors affecting the clinical outco""]",reference_item,0.85,reference_zone,reference_like,reference_numeric_dot,True,True
12,3,reference_content,"5. Cho NS, Yi JW, Lee BG, Rhee YG. Retear patterns after arthroscopic rotator cuff repair: single-row versus suture bridge technique. Am J Sports Med 2010;38:664-671. doi:10.1177/0363546509350081.","[139, 270, 1021, 381]",reference_item,0.85,"[""reference content label: 5. Cho NS, Yi JW, Lee BG, Rhee YG. Retear patterns after art""]",reference_item,0.85,reference_zone,reference_like,reference_numeric_dot,True,True
12,4,reference_content,"6. Chona DV, Lakomkin N, Lott A, Workman AD, Henry AC, Kuntz AF, et al. The timing of retears after arthroscopic rotator cuff repair. J Shoulder Elbow Surg 2017;26:2054-2059. doi: 10.1016/j.jse.2017.0","[138, 393, 1037, 505]",reference_item,0.85,"[""reference content label: 6. Chona DV, Lakomkin N, Lott A, Workman AD, Henry AC, Kuntz""]",reference_item,0.85,reference_zone,reference_like,reference_numeric_dot,True,True
12,5,reference_content,"7. Chung SW, Kim JY, Kim MH, Kim SH, Oh JH. Arthroscopic repair of massive rotator cuff tears: outcome and analysis of factors associated with healing failure or poor postoperative function. Am J Spor","[140, 517, 1046, 670]",reference_item,0.85,"[""reference content label: 7. Chung SW, Kim JY, Kim MH, Kim SH, Oh JH. Arthroscopic rep""]",reference_item,0.85,reference_zone,reference_like,reference_numeric_dot,True,True
12,6,reference_content,"8. Cofield RH, Parvizi J, Hoffmeyer PJ, Lanzer WL, Ilstrup DM, Rowland CM. Surgical repair of chronic rotator cuff tears. A prospective long-term study. J Bone Joint Surg Am. 2001;83:71-77.","[138, 684, 1027, 794]",reference_item,0.85,"[""reference content label: 8. Cofield RH, Parvizi J, Hoffmeyer PJ, Lanzer WL, Ilstrup D""]",reference_item,0.85,reference_zone,reference_like,reference_numeric_dot,True,True
12,7,reference_content,"9. Collin P, Betz M, Herve A, Walch G, Mansat P, Favard L, et al. Clinical and structural outcome 20 years after repair of massive rotator cuff tears. J Shoulder Elbow Surg 2020;29:521-526. doi: $ \u","[137, 808, 1012, 918]",reference_item,0.85,"[""reference content label: 9. Collin P, Betz M, Herve A, Walch G, Mansat P, Favard L, e""]",reference_item,0.85,reference_zone,reference_like,reference_numeric_dot,True,True
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13,9,reference_content,"22. Inui A, Nishimoto H, Mifune Y, Yoshikawa T, Shinohara I, Furukawa T, et al. Screening for osteoporosis from blood test data in elderly women using a machine learning approach. Bioengineering (Base","[138, 1015, 1028, 1128]",reference_item,0.85,"[""reference content label: 22. Inui A, Nishimoto H, Mifune Y, Yoshikawa T, Shinohara I,""]",reference_item,0.85,reference_zone,reference_like,reference_numeric_dot,True,True
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14,2,reference_content,"26. Kunze KN, Police EM, Ranawat AS, Randsborg PH, Williams RJ, Allen AA, et al. Application of machine learning algorithms to predict clinically meaningful improvement after arthroscopic anterior cru","[140, 145, 1013, 299]",reference_item,0.85,"[""reference content label: 26. Kunze KN, Police EM, Ranawat AS, Randsborg PH, Williams ""]",reference_item,0.85,reference_zone,reference_like,reference_numeric_dot,True,True
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14,9,reference_content,"33. Quddusi A, Eversdijk HAJ, Klukowska AM, de Wispelaere MP, Kernbach JM, Schröder ML, et al. External validation of a prediction model for pain and functional outcome after elective lumbar spinal fu","[140, 1140, 1032, 1290]",reference_item,0.85,"[""reference content label: 33. Quddusi A, Eversdijk HAJ, Klukowska AM, de Wispelaere MP""]",reference_item,0.85,reference_zone,reference_like,reference_numeric_dot,True,True
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14,11,reference_content,35. Sevinç E. An empowered AdaBoost algorithm implementation: a COVID-19 dataset study. Comput Ind Eng 2022;165:107912. doi: $ \underline{\text{10.1016/j.cie.2021.107912.}} $,"[139, 1470, 995, 1540]",reference_item,0.85,"[""reference content label: 35. Sevin\u00e7 E. An empowered AdaBoost algorithm implementation""]",reference_item,0.85,reference_zone,reference_like,reference_numeric_dot,True,True
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15,2,reference_content,"36. Shimizu T, Suda K, Maki S, Koda M, Matsumoto Harmon S, Komatsu M, et al. Efficacy of a machine learning-based approach in predicting neurological prognosis of cervical spinal cord injury patients ","[140, 146, 1046, 298]",reference_item,0.85,"[""reference content label: 36. Shimizu T, Suda K, Maki S, Koda M, Matsumoto Harmon S, K""]",reference_item,0.85,reference_zone,reference_like,reference_numeric_dot,True,True
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15,5,reference_content,"39. Takeuchi N, Kozono N, Nishii A, Matsuura K, Ishitani E, Onizuka T, et al. Stump classification was correlated with retear in the suture-bridge and double-row repair techniques for arthroscopic rot","[139, 602, 1031, 753]",reference_item,0.85,"[""reference content label: 39. Takeuchi N, Kozono N, Nishii A, Matsuura K, Ishitani E, ""]",reference_item,0.85,reference_zone,reference_like,reference_numeric_dot,True,True
15,6,reference_content,"40. Tseng PY, Chen YT, Wang CH, Chiu KM, Peng YS, Hsu SP, et al. Prediction of the development of acute kidney injury following cardiac surgery by machine learning. Crit Care 2020;24:478. doi: $ \und","[138, 768, 1020, 876]",reference_item,0.85,"[""reference content label: 40. Tseng PY, Chen YT, Wang CH, Chiu KM, Peng YS, Hsu SP, et""]",reference_item,0.85,reference_zone,reference_like,reference_numeric_dot,True,True
15,7,reference_content,"41. Yan J, Xu Y, Cheng Q, Jiang S, Wang Q, Xiao Y, et al. LightGBM: accelerated genomically designed crop breeding through ensemble learning. Genome Biol 2021;22:271. doi: 10.1186/s13059-021-02492-y.","[138, 892, 974, 1000]",reference_item,0.85,"[""reference content label: 41. Yan J, Xu Y, Cheng Q, Jiang S, Wang Q, Xiao Y, et al. Li""]",reference_item,0.85,reference_zone,reference_like,reference_numeric_dot,True,True
15,8,reference_content,"42. Zhang C, Lei X, Liu L. Predicting metabolite-disease associations based on LightGBM model. Front Genet 2021;12:660275. doi: $ \underline{\text{10.3389/fgene.2021.660275.}} $","[138, 1015, 1020, 1085]",reference_item,0.85,"[""reference content label: 42. Zhang C, Lei X, Liu L. Predicting metabolite-disease ass""]",reference_item,0.85,reference_zone,reference_like,reference_numeric_dot,True,True
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16,3,text,"Table.1 Statistical analysis of patient background in the presence or absence of rotator cuff re-tears. Mean ± standard deviation of each parameter. N.S.: not significant
Table 2. Accuracy and the ar","[137, 186, 1028, 338]",body_paragraph,0.6,"[""default body_paragraph for text label""]",body_paragraph,0.6,,unknown_like,none,True,True
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16,5,text,Fig. 1. Representative magnetic resonance imaging of stump classification. (a) Comparison of signal intensity between deltoid (D; red-circled area) and rotator cuff tears (C; orange-circled area). (b),"[138, 435, 1052, 548]",figure_caption,0.9,"[""figure caption candidate (body narrative): Fig. 1. Representative magnetic resonance imaging of stump c""]",figure_caption_candidate,0.9,display_zone,legend_like,figure_number,True,True
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16,7,text,"Fig. 3. Heat map of the correlation. Stump type, diabetes mellitus (DM), tear size, and fatty degeneration positively correlated with rotator cuff re-tear.","[139, 601, 1027, 672]",figure_caption,0.9,"[""figure caption candidate (body narrative): Fig. 3. Heat map of the correlation. Stump type, diabetes me""]",figure_caption_candidate,0.9,display_zone,legend_like,figure_number,True,True
16,8,text,Fig. 4. ROC curve of each trained model,"[139, 683, 542, 713]",figure_caption,0.9,"[""figure prefix matched: Fig. 4. ROC curve of each trained model""]",figure_caption,0.9,display_zone,legend_like,figure_number,True,True
16,9,text,"Fig. 5. (a) Permutation features the importance of light Gradient Boosting Machine (LightGBM) model. Important features have larger scores. Top three important features were age, stump type, and tear ","[137, 725, 1048, 922]",figure_caption,0.9,"[""figure caption candidate (body narrative): Fig. 5. (a) Permutation features the importance of light Gra""]",figure_caption_candidate,0.9,display_zone,legend_like,figure_number,True,True
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17,1,table,"<table><tr><td></td><td>Re-tear (n=45)</td><td>No re-tear (n=308)</td><td>P value</td></tr><tr><td colspan=""4"">Features</td></tr><tr><td>Age</td><td>$ 67.5 \pm 9.0 $</td><td>$ 63.0 \pm 8.9 $</td><","[100, 212, 1087, 866]",media_asset,0.85,"[""media label: table""]",media_asset,0.85,,unknown_like,none,True,True
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18,1,table,<table><tr><td>ML model</td><td>logistic regression (95% CI)</td><td>random forest (95% CI)</td><td>AdaBoost (95% CI)</td><td>CatBoost (95% CI)</td><td>LightGBM (95% CI)</td></tr><tr><td>Accuracy</td>,"[96, 214, 1051, 472]",media_asset,0.85,"[""media label: table""]",media_asset,0.85,,unknown_like,none,True,True
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2 2 0 header Journal Pre-proof [503, 57, 689, 82] frontmatter_noise 0.98 ["journal pre-proof running header suppressed: p2 y=57/1584", "page_1_preproof_cover_dropped_upstream"] frontmatter_noise 0.98 preproof_cover_zone unknown_like preproof_marker False False
3 2 1 doc_title Re-tear after arthroscopic rotator cuff tear surgery: risk analysis using machine learning [150, 145, 1071, 175] paper_title 0.8 ["page-1 zone title_zone: Re-tear after arthroscopic rotator cuff tear surgery: risk a"] paper_title 0.8 frontmatter_main_zone support_like none True True
4 2 2 text Issei Shinohara, M.D., Ph.D. $ ^{1} $, Yutaka Mifune $ ^{*} $, M.D., Ph.D. $ ^{1} $, Atsuyuki Inui, M.D., Ph.D. $ ^{1} $, Hanako Nishimoto, M.D., Ph.D. $ ^{1} $, Tomoya Yoshikawa, M.D., Ph.D. $ ^{1} $ [137, 259, 1069, 456] authors 0.8 ["page-1 zone author_zone: Issei Shinohara, M.D., Ph.D. $ ^{1} $, Yutaka Mifune $ ^{*} "] authors 0.8 frontmatter_main_zone support_like none True True
5 2 3 text 1. Department of Orthopaedic Surgery, Kobe University Graduate School of Medicine, 5-2, Kusunoki-cho7, Chuo-ku, Kobe, Hyogo 650-0017, Japan [140, 482, 1041, 556] affiliation 0.8 ["page-1 zone affiliation_zone: 1. Department of Orthopaedic Surgery, Kobe University Gradua"] affiliation 0.8 body_zone reference_like reference_numeric_dot True True
6 2 4 text 2. Department of Orthopaedic Surgery, Himeji St Mary's Hospital, Himeji, Hyogo, Japan **These authors contributed equally to this work.** [138, 581, 1017, 653] frontmatter_support 0.78 ["first-surviving-page support text: 2. Department of Orthopaedic Surgery, Himeji St Mary's Hospi"] frontmatter_support 0.78 body_zone reference_like reference_numeric_dot True True
7 2 5 text *Correspondence to: Yutaka Mifune, MD, PhD, Department of Orthopaedic Surgery, Kobe University Graduate School of Medicine, 7-5-1 Kusunoki-cho, Chuo-ku, Kobe, 650-0017 Hyogo, Japan; Phone: +81-78-382- [138, 680, 1089, 796] frontmatter_noise 0.8 ["page-1 zone journal_furniture_zone: *Correspondence to: Yutaka Mifune, MD, PhD, Department of Or"] frontmatter_noise 0.8 frontmatter_main_zone support_like none False False
8 2 6 text The Kobe University Graduate School of Medicine Ethics Committee approved this study (no. 1735). [139, 878, 1056, 948] affiliation 0.8 ["page-1 zone affiliation_zone: The Kobe University Graduate School of Medicine Ethics Commi"] affiliation 0.8 frontmatter_main_zone support_like none True True
9 2 7 text Running-head title: Machine learning predicts rotator cuff re-tear [138, 977, 775, 1007] body_paragraph 0.6 ["default body_paragraph for text label"] body_paragraph 0.6 frontmatter_main_zone support_like none True True
10 2 8 paragraph_title Disclaimers: [139, 1035, 276, 1063] sub_subsection_heading 0.6 ["unnumbered paragraph_title, inferred level sub_subsection_heading: Disclaimers:"] sub_subsection_heading 0.6 frontmatter_main_zone heading_like short_fragment True True
11 2 9 text Funding: No funding was disclosed by the authors. [137, 1092, 636, 1122] body_paragraph 0.6 ["default body_paragraph for text label"] body_paragraph 0.6 frontmatter_main_zone support_like none True True
12 2 10 text Conflicts of interest: The authors, their immediate families, and any research foundation with which they are affiliated have not received any financial payments or other benefits from any commercial [137, 1149, 1044, 1262] body_paragraph 0.6 ["default body_paragraph for text label"] body_paragraph 0.6 body_zone body_like none True True
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15 3 2 abstract Re-tear after arthroscopic rotator cuff tear surgery: risk analysis using machine learning [168, 143, 1019, 177] abstract_body 0.85 ["abstract label from Paddle OCR"] abstract_body 0.85 body_zone body_like none True True
16 3 3 paragraph_title Abstract [141, 225, 241, 257] abstract_heading 0.95 ["abstract heading"] abstract_heading 0.95 body_zone heading_like short_fragment True True
17 3 4 abstract Background: Postoperative rotator cuff re-tear after arthroscopic rotator cuff repair (ARCR) is still a major problem. Various risk factors such as age, gender, and tear size have been reported. Recen [136, 269, 1053, 1533] abstract_body 0.85 ["abstract label from Paddle OCR"] abstract_body 0.85 body_zone body_like none True True
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20 4 1 header Journal Pre-proof [480, 61, 677, 88] frontmatter_noise 0.98 ["journal pre-proof running header suppressed: p4 y=61/1684"] frontmatter_noise 0.98 preproof_cover_zone unknown_like preproof_marker False False
21 4 2 text Conclusions: The ML classifier model predicted re-tears after ARCR with high accuracy, and the AI model showed that the most important characteristics affecting re-tears were age and imaging findings, [133, 147, 1049, 561] body_paragraph 0.6 ["default body_paragraph for text label"] body_paragraph 0.6 body_zone body_like none True True
22 4 3 text Postoperative re-tear is still a problem in arthroscopic rotator cuff repair (ARCR) for degenerative rotator cuff tears (RCTs). The reported re-tear rate after ARCR varies depending on the suture meth [137, 754, 1052, 1503] body_paragraph 0.6 ["default body_paragraph for text label"] body_paragraph 0.6 body_zone body_like none True True
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26 5 2 text classification by MRI images. There are few reports taking stump classification into account that may be useful for predicting re-tears after ARCR. In this study, we focused on the analysis of clinica [138, 149, 1051, 1118] body_paragraph 0.6 ["default body_paragraph for text label"] body_paragraph 0.6 body_zone body_like none True True
27 5 3 paragraph_title Materials and Methods [141, 1251, 386, 1278] section_heading 0.9 ["explicit scholarly heading: Materials and Methods"] section_heading 0.9 body_zone heading_like canonical_section_name True True
28 5 4 text Ethical approval [141, 1305, 311, 1334] unknown_structural 0.3 ["short text, uncertain role"] unknown_structural 0.3 body_zone body_like short_fragment False True
29 5 5 text This study was approved by the appropriate review board, and informed consent was obtained from all patients involved. [140, 1358, 1049, 1444] body_paragraph 0.6 ["default body_paragraph for text label"] body_paragraph 0.6 body_zone body_like none True True
30 5 6 text Data collection [141, 1470, 296, 1498] unknown_structural 0.3 ["short text, uncertain role"] unknown_structural 0.3 body_zone body_like short_fragment False True
31 5 7 number 3 [588, 1549, 603, 1570] noise 0.9 ["page number label"] noise 0.9 body_zone body_like short_fragment False False
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33 6 1 header Journal Pre-proof [480, 61, 677, 88] frontmatter_noise 0.98 ["journal pre-proof running header suppressed: p6 y=61/1684"] frontmatter_noise 0.98 preproof_cover_zone unknown_like preproof_marker False False
34 6 2 text Patients who underwent ARCR for degenerative complete rotator cuff tears from April 2017 to June 2021 at our institution or affiliated institutions were included. ARCR was performed by two surgeons, Y [138, 144, 1048, 662] body_paragraph 0.6 ["default body_paragraph for text label"] body_paragraph 0.6 body_zone body_like none True True
35 6 3 text Each patient parameter is expressed as mean ± standard deviation. To compare patient backgrounds with and without re-tears, the Mann–Whitney U test was used to compare two variables (e.g., gender) and [137, 699, 1030, 894] body_paragraph 0.6 ["default body_paragraph for text label"] body_paragraph 0.6 body_zone body_like none True True
36 6 4 paragraph_title Machine learning [141, 918, 321, 948] subsection_heading 0.6 ["unnumbered paragraph_title, inferred level subsection_heading: Machine learning"] subsection_heading 0.6 body_zone heading_like short_fragment True True
37 6 5 text The data collection and ML workflow are shown in Fig. 2. Five supervised algorithms were applied to validate clinical data $ ^{20,22} $ (logistic regression, random forest, $ ^{1} $ adaptive boost (Ad [137, 973, 1054, 1502] body_paragraph 0.6 ["default body_paragraph for text label"] body_paragraph 0.6 body_zone body_like none True True
38 6 6 number 4 [588, 1549, 603, 1570] noise 0.9 ["page number label"] noise 0.9 body_zone body_like short_fragment False False
39 7 0 aside_text 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 [69, 145, 115, 1499] unknown_structural 0.2 ["unrecognized label 'aside_text'"] unknown_structural 0.2 reference_like reference_numeric_dot False True
40 7 1 header Journal Pre-proof [480, 61, 677, 88] frontmatter_noise 0.98 ["journal pre-proof running header suppressed: p7 y=61/1684"] frontmatter_noise 0.98 preproof_cover_zone unknown_like preproof_marker False False
41 7 2 text method that attempts to obtain better predictions by using multiple training models and performing majority voting on the results. $ ^{1} $ On the other hand, Adaboost, Catboost, and LightGBM are grad [135, 145, 1059, 1504] body_paragraph 0.6 ["default body_paragraph for text label"] body_paragraph 0.6 body_zone body_like none True True
42 7 3 number 5 [587, 1548, 603, 1570] noise 0.9 ["page number label"] noise 0.9 body_zone body_like short_fragment False False
43 8 0 aside_text 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 [68, 146, 115, 1504] unknown_structural 0.2 ["unrecognized label 'aside_text'"] unknown_structural 0.2 reference_like reference_numeric_dot False True
44 8 1 header Journal Pre-proof [481, 61, 676, 88] frontmatter_noise 0.98 ["journal pre-proof running header suppressed: p8 y=61/1684"] frontmatter_noise 0.98 preproof_cover_zone unknown_like preproof_marker False False
45 8 2 text randomly shuffled; $ ^{12} $ the Shapley additive explanation (SHAP) value is defined as the contribution of each feature to the model prediction based on game theory. $ ^{40} $ Briefly, it is a metho [137, 144, 1037, 340] body_paragraph 0.6 ["default body_paragraph for text label"] body_paragraph 0.6 body_zone body_like none True True
46 8 3 paragraph_title Results Study participants and statistical analysis [138, 422, 550, 508] subsection_heading 0.6 ["unnumbered paragraph_title, inferred level subsection_heading: Results Study participants and statistical analysis"] subsection_heading 0.6 body_zone heading_like none True True
47 8 4 footer [138, 477, 550, 508] noise 0.9 ["footer label"] noise 0.9 body_zone body_like empty False False
48 8 5 text Of the 582 cases who underwent ARCR at our institution or affiliated institutions, 353 were finally included after excluding re-tears (12 cases), traumatic tears (182 cases), and patients who required [137, 531, 1052, 837] body_paragraph 0.6 ["default body_paragraph for text label"] body_paragraph 0.6 body_zone body_like none True True
49 8 6 paragraph_title Prediction of rotator cuff re-tear in each ML model [139, 873, 641, 904] subsection_heading 0.6 ["unnumbered paragraph_title, inferred level subsection_heading: Prediction of rotator cuff re-tear in each ML model"] subsection_heading 0.6 body_zone heading_like none True True
50 8 7 text Fig. 3 shows a heat map representing the correlation between each parameter and rotator cuff re-tear. Warm colors indicate a positive correlation, while cold colors indicate a negative correlation. Th [137, 923, 1052, 1283] body_paragraph 0.9 ["figure caption candidate (body narrative): Fig. 3 shows a heat map representing the correlation between"] figure_caption_candidate 0.9 display_zone legend_like figure_number True True
51 8 8 paragraph_title Important features of the predictor variables [140, 1310, 576, 1343] subsection_heading 0.6 ["unnumbered paragraph_title, inferred level subsection_heading: Important features of the predictor variables"] subsection_heading 0.6 body_zone heading_like none True True
52 8 9 text To detect the importance of each parameter for predicting postoperative rotator cuff re-tear, feature importance was calculated for the LightGBM model, which showed the highest AUC. Age, stump classif [137, 1362, 1042, 1510] body_paragraph 0.6 ["default body_paragraph for text label"] body_paragraph 0.6 body_zone body_like none True True
53 8 10 number 6 [588, 1549, 603, 1571] noise 0.9 ["page number label"] noise 0.9 body_zone body_like short_fragment False False
54 9 0 aside_text 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 [68, 147, 115, 1496] unknown_structural 0.2 ["unrecognized label 'aside_text'"] unknown_structural 0.2 reference_like reference_numeric_dot False True
55 9 1 header Journal Pre-proof [480, 61, 677, 88] frontmatter_noise 0.98 ["journal pre-proof running header suppressed: p9 y=61/1684"] frontmatter_noise 0.98 preproof_cover_zone unknown_like preproof_marker False False
56 9 2 text associated with postoperative rotator cuff re-tear in the LightGBM model (Fig. 5a). The SHAP score showed stump classification, tear size, and age as important characteristics. As shown in Fig. 5b, st [138, 144, 1053, 343] body_paragraph 0.6 ["default body_paragraph for text label"] body_paragraph 0.6 body_zone body_like none True True
57 9 3 paragraph_title Discussion [140, 476, 256, 506] section_heading 0.9 ["explicit scholarly heading: Discussion"] section_heading 0.9 body_zone heading_like canonical_section_name True True
58 9 4 text The ML classification models predicted re-tears after ARCR with high accuracy. Among the five used models, LightGBM showed the highest AUC. In the LightGBM model, age, stump classification, and tear s [138, 532, 1042, 728] body_paragraph 0.6 ["default body_paragraph for text label"] body_paragraph 0.6 body_zone body_like none True True
59 9 5 text In the last decades, AI techniques based on mathematical modeling have been developed; ML is one of the AI-based approaches, and ML models are increasingly integrated into clinical diagnosis and the p [137, 753, 1054, 1504] body_paragraph 0.6 ["default body_paragraph for text label"] body_paragraph 0.6 body_zone body_like none True True
60 9 6 number 7 [588, 1548, 603, 1570] noise 0.9 ["page number label"] noise 0.9 body_zone body_like short_fragment False False
61 10 0 aside_text 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 [69, 145, 115, 1497] unknown_structural 0.2 ["unrecognized label 'aside_text'"] unknown_structural 0.2 reference_like reference_numeric_dot False True
62 10 1 header Journal Pre-proof [480, 61, 677, 88] frontmatter_noise 0.98 ["journal pre-proof running header suppressed: p10 y=61/1684"] frontmatter_noise 0.98 preproof_cover_zone unknown_like preproof_marker False False
63 10 2 text framework that uses a decision-tree-based learning algorithm, adopting a histogram algorithm and a depth-limited leaf-wise leaf growth strategy. $ ^{41} $ This strategy increases computational efficie [134, 153, 1054, 1499] body_paragraph 0.6 ["default body_paragraph for text label"] body_paragraph 0.6 body_zone body_like none True True
64 10 3 number 8 [588, 1548, 603, 1570] noise 0.9 ["page number label"] noise 0.9 body_zone body_like short_fragment False False
65 11 0 aside_text 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 [68, 146, 115, 1479] unknown_structural 0.2 ["unrecognized label 'aside_text'"] unknown_structural 0.2 reference_zone reference_like reference_numeric_dot False True
66 11 1 header Journal Pre-proof [480, 61, 677, 88] frontmatter_noise 0.98 ["journal pre-proof running header suppressed: p11 y=61/1684"] frontmatter_noise 0.98 preproof_cover_zone unknown_like preproof_marker False False
67 11 2 text This study has some limitations. First, although the model performed well on the present data set, the number of cases in the original data is not large. Second, we did not consider factors by procedu [137, 146, 1055, 564] body_paragraph 0.6 ["default body_paragraph for text label"] body_paragraph 0.6 body_zone body_like none True True
68 11 3 paragraph_title Conclusion [139, 641, 264, 670] section_heading 0.9 ["explicit scholarly heading: Conclusion"] section_heading 0.9 body_zone heading_like canonical_section_name True True
69 11 4 text The ML classifier model predicted re-tears after ARCR with high accuracy, and the AI model showed that the most important characteristics affecting re-tears were age and imaging findings, including st [136, 698, 1053, 1004] body_paragraph 0.6 ["default body_paragraph for text label"] body_paragraph 0.6 body_zone body_like none True True
70 11 5 paragraph_title References [140, 1083, 260, 1112] reference_heading 0.9 ["references heading: References"] reference_heading 0.9 reference_zone heading_like short_fragment True True
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74 11 9 number 9 [588, 1548, 603, 1569] noise 0.9 ["page number label"] noise 0.9 tail_nonref_hold_zone unknown_like short_fragment False False
75 12 0 aside_text 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 [67, 145, 114, 1538] unknown_structural 0.2 ["unrecognized label 'aside_text'"] unknown_structural 0.2 reference_zone reference_like reference_numeric_dot False True
76 12 1 header Journal Pre-proof [479, 61, 678, 88] frontmatter_noise 0.98 ["journal pre-proof running header suppressed: p12 y=61/1684"] frontmatter_noise 0.98 preproof_cover_zone unknown_like preproof_marker False False
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91 13 2 reference_content 15. Gladstone JN, Bishop JY, Lo IK, Flatow EL. Fatty infiltration and atrophy of the rotator cuff do not improve after rotator cuff repair and correlate with poor functional outcome. Am J Sports Med 2 [142, 146, 1028, 257] reference_item 0.85 ["reference content label: 15. Gladstone JN, Bishop JY, Lo IK, Flatow EL. Fatty infiltr"] reference_item 0.85 reference_zone reference_like reference_numeric_dot True True
92 13 3 reference_content 17. Goutallier D, Postel JM, Bernageau J, Lavau L, Voisin MC. Fatty muscle degeneration in cuff ruptures. Pre- and postoperative evaluation by CT scan. Clin Orthop Relat Res 1994;304:78-83. [141, 269, 1039, 379] reference_item 0.85 ["reference content label: 17. Goutallier D, Postel JM, Bernageau J, Lavau L, Voisin MC"] reference_item 0.85 reference_zone reference_like reference_numeric_dot True True
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94 13 5 reference_content 18. Gowd AK, Agarwalla A, Beck EC, Rosas S, Waterman BR, Romeo AA, et al. Prediction of total healthcare cost following total shoulder arthroplasty utilizing machine learning. J Shoulder Elbow Surg 20 [143, 559, 1035, 671] reference_item 0.85 ["reference content label: 18. Gowd AK, Agarwalla A, Beck EC, Rosas S, Waterman BR, Rom"] reference_item 0.85 reference_zone reference_like reference_numeric_dot True True
95 13 6 reference_content 19. Gumina S, Carbone S, Campagna V, Candela V, Sacchetti FM, Giannicola G. The impact of aging on rotator cuff tear size. Musculoskelet Surg 2013;97;Suppl 1:69-72. doi:10.1007/s12306-013-0263-2. [142, 684, 1037, 794] reference_item 0.85 ["reference content label: 19. Gumina S, Carbone S, Campagna V, Candela V, Sacchetti FM"] reference_item 0.85 reference_zone reference_like reference_numeric_dot True True
96 13 7 reference_content 20. Hancock JT, Khoshgoftaar TM. CatBoost for big data: an interdisciplinary review. J Big Data 2020;7:94. doi: $ \underline{\text{10.1186/s40537-020-00369-8.}} $ [137, 807, 1032, 877] reference_item 0.85 ["reference content label: 20. Hancock JT, Khoshgoftaar TM. CatBoost for big data: an i"] reference_item 0.85 reference_zone reference_like reference_numeric_dot True True
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98 13 9 reference_content 22. Inui A, Nishimoto H, Mifune Y, Yoshikawa T, Shinohara I, Furukawa T, et al. Screening for osteoporosis from blood test data in elderly women using a machine learning approach. Bioengineering (Base [138, 1015, 1028, 1128] reference_item 0.85 ["reference content label: 22. Inui A, Nishimoto H, Mifune Y, Yoshikawa T, Shinohara I,"] reference_item 0.85 reference_zone reference_like reference_numeric_dot True True
99 13 10 reference_content 23. Ishitani E, Harada N, Sonoda Y, Okada F, Yara T, Katsuki I. Tendon stump type on magnetic resonance imaging is a predictive factor for retear after arthroscopic rotator cuff repair. J Shoulder Elb [139, 1140, 1034, 1290] reference_item 0.85 ["reference content label: 23. Ishitani E, Harada N, Sonoda Y, Okada F, Yara T, Katsuki"] reference_item 0.85 reference_zone reference_like reference_numeric_dot True True
100 13 11 reference_content 24. Jeong HY, Kim HJ, Jeon YS, Rhee YG. Factors predictive of healing in large rotator cuff tears: is it possible to predict retear preoperatively? Am J Sports Med 2018;46:1693-1700. doi: $ \underlin [139, 1304, 1036, 1415] reference_item 0.85 ["reference content label: 24. Jeong HY, Kim HJ, Jeon YS, Rhee YG. Factors predictive o"] reference_item 0.85 reference_zone reference_like reference_numeric_dot True True
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1,7,text,"OBJECTIVE To assess the validity and timeliness of the SPM compared with SIRS, qSOFA, and SOFA.","[90, 484, 794, 510]",frontmatter_noise,0.7,"[""keyword-like block: OBJECTIVE To assess the validity and timeliness of the SPM c""]",frontmatter_noise,0.7,frontmatter_main_zone,support_like,none,False,False
1,8,text,"DESIGN, SETTING, AND PARTICIPANTS This retrospective cohort study included all adults admitted to 5 acute care hospitals in a single US health system between June 5, 2019, and December 31, 2020. Data ","[90, 537, 793, 615]",unknown_structural,0.8,"[""page-1 zone author_zone: DESIGN, SETTING, AND PARTICIPANTS This retrospective cohort ""]",authors,0.8,body_zone,body_like,none,False,True
1,9,text,"MAIN OUTCOMES AND MEASURES A sepsis event was defined as receipt of 4 or more days of antimicrobials, blood cultures collected within ±48 hours of initial antimicrobial, and at least 1 organ dysfuncti","[89, 640, 796, 745]",body_paragraph,0.6,"[""default body_paragraph for text label""]",body_paragraph,0.6,body_zone,body_like,none,True,True
1,10,text,"RESULTS Of 60 507 total admissions, 1663 (2.7%) met sepsis criteria, with 1324 electronic health record-confirmed sepsis (699 [52.8%] male patients; 298 [22.5%] Black patients; 46 [3.5%] Hispanic/Lati","[88, 770, 792, 1135]",body_paragraph,0.6,"[""default body_paragraph for text label""]",body_paragraph,0.6,body_zone,body_like,none,True,True
1,11,text,"CONCLUSIONS AND RELEVANCE In this cohort study of hospital admissions, balanced accuracy of the SPM outperformed other models at higher threshold PSS; however, application of the SPM in a clinical set","[89, 1159, 791, 1263]",body_paragraph,0.6,"[""default body_paragraph for text label""]",body_paragraph,0.6,body_zone,body_like,none,True,True
1,12,paragraph_title,Key Points,"[845, 288, 936, 312]",paper_title,0.7,"[""unnumbered paragraph_title in title zone on page 1: Key Points""]",paper_title,0.7,frontmatter_main_zone,support_like,short_fragment,True,True
1,13,text,Question Does the Sepsis Prediction Model (SPM) outperform other sepsis prediction scores with respect to validity and timeliness?,"[844, 318, 1117, 419]",structured_insert_candidate,0.6,"[""default body_paragraph for text label""]",body_paragraph,0.6,frontmatter_main_zone,support_like,none,False,False
1,14,text,Findings This cohort study of 60 507 adult admissions found that although balanced accuracy of the SPM at a predicting sepsis score (PSS) threshold of 8 or greater was better than that of the quick Se,"[844, 432, 1112, 743]",authors,0.8,"[""page-1 zone author_zone: Findings This cohort study of 60 507 adult admissions found ""]",authors,0.8,frontmatter_main_zone,support_like,none,True,True
1,15,text,"Meaning While the balanced accuracy of the SPM was better than qSOFA, SOFA, and SIRS at higher-threshold PSS, it had poor timeliness for sepsis prediction.","[844, 754, 1114, 884]",structured_insert_candidate,0.6,"[""default body_paragraph for text label""]",body_paragraph,0.6,body_zone,body_like,none,False,False
1,16,text,✗ Invited Commentary,"[835, 920, 1002, 945]",body_paragraph,0.6,"[""default body_paragraph for text label""]",body_paragraph,0.6,body_zone,body_like,none,True,True
1,17,text,Supplemental content,"[835, 958, 1012, 982]",body_paragraph,0.6,"[""default body_paragraph for text label""]",body_paragraph,0.6,body_zone,body_like,none,True,True
1,18,text,Author affiliations and article information are listed at the end of this article.,"[833, 995, 1098, 1036]",body_paragraph,0.6,"[""default body_paragraph for text label""]",body_paragraph,0.6,body_zone,body_like,none,True,True
1,19,footnote,JAMA Network Open. 2023;6(8):e2329729. doi:10.1001/jamanetworkopen.2023.29729,"[89, 1289, 598, 1311]",footnote,0.7,"[""footnote label: JAMA Network Open. 2023;6(8):e2329729. doi:10.1001/jamanetwo""]",footnote,0.7,body_zone,body_like,none,True,True
1,20,footer,☐ Open Access. This is an open access article distributed under the terms of the CC-BY License.,"[91, 1431, 648, 1454]",noise,0.9,"[""footer label""]",noise,0.9,body_zone,body_like,none,False,False
1,21,footer,JAMA Network Open. 2023;6(8):e2329729. doi:10.1001/jamanetworkopen.2023.29729,"[91, 1469, 599, 1490]",noise,0.9,"[""footer label""]",noise,0.9,body_zone,body_like,none,False,False
1,22,footer,"August 25, 2023","[980, 1469, 1082, 1489]",noise,0.9,"[""footer label""]",noise,0.9,body_zone,body_like,short_fragment,False,False
1,23,footer,Downloaded from jamanetwork.com by guest on 05/18/2026,"[2, 1549, 528, 1570]",noise,0.9,"[""footer label""]",noise,0.9,body_zone,body_like,none,False,False
1,24,number,1/13,"[1102, 1470, 1128, 1490]",noise,0.9,"[""page number label""]",noise,0.9,body_zone,body_like,short_fragment,False,False
2,0,header,JAMA Network Open | Health Informatics,"[92, 67, 409, 89]",noise,0.9,"[""header label""]",noise,0.9,frontmatter_side_zone,support_like,none,False,False
2,1,header,"Sepsis Prediction Model vs SIRS, qSOFA, and SOFA","[813, 68, 1128, 88]",noise,0.9,"[""header label""]",noise,0.9,frontmatter_side_zone,support_like,none,False,False
2,2,paragraph_title,Introduction,"[92, 116, 219, 139]",section_heading,0.9,"[""explicit scholarly heading: Introduction""]",section_heading,0.9,frontmatter_side_zone,heading_like,canonical_section_name,True,True
2,3,text,"The Third International Task Force on Sepsis and Septic Shock (Sepsis-3) established a consensus definition of sepsis as ""life-threatening organ dysfunction caused by a dysregulated host response to i","[90, 154, 793, 489]",body_paragraph,0.6,"[""default body_paragraph for text label""]",body_paragraph,0.6,body_zone,body_like,none,True,True
2,4,text,Sepsis identification systems based on bedside findings are widely disseminated to promote early recognition and prompt intervention. Current recommendations emphasize that sepsis be considered a medi,"[90, 493, 794, 750]",body_paragraph,0.6,"[""default body_paragraph for text label""]",body_paragraph,0.6,body_zone,body_like,none,True,True
2,5,text,The Sepsis Prediction Model (SPM; Epic Systems) is a proprietary algorithm developed from a pooled sample of 405 000 patient encounters across 3 health care organizations between 2013 and 2015. $ ^{[3,"[90, 752, 792, 1164]",affiliation,0.8,"[""page-1 zone affiliation_zone: The Sepsis Prediction Model (SPM; Epic Systems) is a proprie""]",affiliation,0.8,frontmatter_main_zone,support_like,none,True,True
2,6,paragraph_title,Methods Background,"[92, 1211, 199, 1275]",subsection_heading,0.6,"[""unnumbered paragraph_title, inferred level subsection_heading: Methods Background""]",subsection_heading,0.6,body_zone,heading_like,short_fragment,True,True
2,7,footer,,"[92, 1252, 199, 1275]",noise,0.9,"[""footer label""]",noise,0.9,body_zone,body_like,empty,False,False
2,8,text,"This is a retrospective cohort study of all adult (>17 years) admissions within 5 hospitals of Wake Forest Baptist Health (Winston-Salem, North Carolina) beginning June 5, 2019, through December 31, 2","[90, 1279, 793, 1407]",body_paragraph,0.6,"[""default body_paragraph for text label""]",body_paragraph,0.6,body_zone,body_like,none,True,True
2,9,footer,JAMA Network Open. 2023;6(8):e2329729. doi:10.1001/jamanetworkopen.2023.29729,"[92, 1468, 619, 1490]",noise,0.9,"[""footer label""]",noise,0.9,,unknown_like,none,False,False
2,10,footer,"August 25, 2023","[978, 1470, 1080, 1489]",noise,0.9,"[""footer label""]",noise,0.9,body_zone,body_like,short_fragment,False,False
2,11,footer,Downloaded from jamanetwork.com by guest on 05/18/2026,"[2, 1549, 528, 1570]",noise,0.9,"[""footer label""]",noise,0.9,body_zone,body_like,none,False,False
2,12,number,2/13,"[1099, 1470, 1128, 1489]",noise,0.9,"[""page number label""]",noise,0.9,body_zone,body_like,short_fragment,False,False
3,0,header,JAMA Network Open | Health Informatics,"[93, 67, 409, 89]",noise,0.9,"[""header label""]",noise,0.9,body_zone,body_like,none,False,False
3,1,header,"Sepsis Prediction Model vs SIRS, qSOFA, and SOFA","[813, 68, 1128, 89]",noise,0.9,"[""header label""]",noise,0.9,body_zone,body_like,none,False,False
3,2,text,study followed the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting guidelines. $ ^{35} $,"[90, 114, 791, 164]",body_paragraph,0.6,"[""default body_paragraph for text label""]",body_paragraph,0.6,body_zone,body_like,none,True,True
3,3,paragraph_title,Study Population,"[91, 192, 241, 215]",subsection_heading,0.6,"[""unnumbered paragraph_title, inferred level subsection_heading: Study Population""]",subsection_heading,0.6,body_zone,heading_like,short_fragment,True,True
3,4,text,Structured data from the EHR identified inpatient admissions and all associated characteristics reported in this study. We handled potential missing data exactly how an applied algorithm would handle ,"[90, 218, 783, 502]",body_paragraph,0.6,"[""default body_paragraph for text label""]",body_paragraph,0.6,body_zone,body_like,none,True,True
3,5,paragraph_title,Exclusion Criteria,"[91, 529, 242, 551]",subsection_heading,0.6,"[""unnumbered paragraph_title, inferred level subsection_heading: Exclusion Criteria""]",subsection_heading,0.6,body_zone,heading_like,short_fragment,True,True
3,6,text,"For the analysis, we excluded admissions to a Burn Service, length of stay more than 30 days, those who left against medical advice, and transfers from an out-of-system facility or between internal si","[90, 556, 785, 710]",body_paragraph,0.6,"[""default body_paragraph for text label""]",body_paragraph,0.6,body_zone,body_like,none,True,True
3,7,paragraph_title,Sepsis Definition,"[91, 737, 238, 760]",subsection_heading,0.6,"[""unnumbered paragraph_title, inferred level subsection_heading: Sepsis Definition""]",subsection_heading,0.6,body_zone,heading_like,short_fragment,True,True
3,8,text,EHR-confirmed sepsis was defined by the Centers for Disease Control and Prevention (CDC) Adult Sepsis Event (ASE) criteria of (1) 4 or more days of qualifying antimicrobial therapy beginning within $,"[88, 763, 796, 1075]",body_paragraph,0.6,"[""default body_paragraph for text label""]",body_paragraph,0.6,body_zone,body_like,none,True,True
3,9,paragraph_title,Electronic Sepsis Alert Systems,"[91, 1101, 355, 1124]",subsection_heading,0.6,"[""unnumbered paragraph_title, inferred level subsection_heading: Electronic Sepsis Alert Systems""]",subsection_heading,0.6,body_zone,heading_like,none,True,True
3,10,text,"An electronic sepsis alert based on the SPM was active during all phases of care at a single site within the health system, with the alert threshold set to a PSS of 10 or greater. All other study site","[90, 1127, 790, 1255]",body_paragraph,0.6,"[""default body_paragraph for text label""]",body_paragraph,0.6,body_zone,body_like,none,True,True
3,11,paragraph_title,Time Zero,"[92, 1283, 182, 1305]",subsection_heading,0.6,"[""unnumbered paragraph_title, inferred level subsection_heading: Time Zero""]",subsection_heading,0.6,body_zone,heading_like,short_fragment,True,True
3,12,text,"Time zero was set as 15 minutes prior to first clinician action, as defined by the initial order for antimicrobials or blood cultures. As stated previously, the SPM recalculates the PSS every 15 minut","[90, 1308, 794, 1436]",body_paragraph,0.6,"[""default body_paragraph for text label""]",body_paragraph,0.6,body_zone,body_like,none,True,True
3,13,footer,JAMA Network Open. 2023;6(8):e2329729. doi:10.1001/jamanetworkopen.2023.29729,"[92, 1468, 619, 1490]",noise,0.9,"[""footer label""]",noise,0.9,,unknown_like,none,False,False
3,14,footer,"August 25, 2023","[979, 1470, 1080, 1489]",noise,0.9,"[""footer label""]",noise,0.9,body_zone,body_like,short_fragment,False,False
3,15,footer,Downloaded from jamanetwork.com by guest on 05/18/2026,"[2, 1549, 528, 1570]",noise,0.9,"[""footer label""]",noise,0.9,body_zone,body_like,none,False,False
3,16,number,3/13,"[1099, 1470, 1128, 1489]",noise,0.9,"[""page number label""]",noise,0.9,body_zone,body_like,short_fragment,False,False
4,0,header_image,,"[95, 0, 252, 19]",unknown_structural,0.2,"[""unrecognized label 'header_image'""]",unknown_structural,0.2,body_zone,unknown_like,empty,False,True
4,1,header,JAMA Network Open | Health Informatics,"[93, 67, 409, 89]",noise,0.9,"[""header label""]",noise,0.9,body_zone,body_like,none,False,False
4,2,header,"Sepsis Prediction Model vs SIRS, qSOFA, and SOFA","[813, 69, 1128, 88]",noise,0.9,"[""header label""]",noise,0.9,body_zone,body_like,none,False,False
4,3,text,"action related to suspicion or treatment of sepsis. For example, if a clinician assessed a patient and ordered antibiotics or a blood culture at 2:00 PM, time zero for that episode of care would be se","[91, 115, 785, 188]",body_paragraph,0.6,"[""default body_paragraph for text label""]",body_paragraph,0.6,body_zone,body_like,none,True,True
4,4,text,"The times elapsed for SIRS, qSOFA, and SOFA to reach threshold score were assessed at 1 hour. These intervals reflected the time from when any first criterion was present to the time when 2 or more cr","[89, 193, 786, 319]",body_paragraph,0.6,"[""default body_paragraph for text label""]",body_paragraph,0.6,body_zone,body_like,none,True,True
4,5,paragraph_title,Outcomes,"[91, 350, 185, 370]",sub_subsection_heading,0.6,"[""unnumbered paragraph_title, inferred level sub_subsection_heading: Outcomes""]",sub_subsection_heading,0.6,body_zone,heading_like,short_fragment,True,True
4,6,text,"Our primary goal was to compare the performance of the SPM in classifying sepsis admissions relative to SIRS, qSOFA, and SOFA, and examine the timeliness of each tool with respect to time zero. Perfor","[90, 375, 793, 476]",body_paragraph,0.6,"[""default body_paragraph for text label""]",body_paragraph,0.6,body_zone,body_like,none,True,True
4,7,paragraph_title,Statistical Analysis,"[92, 504, 252, 526]",subsection_heading,0.6,"[""unnumbered paragraph_title, inferred level subsection_heading: Statistical Analysis""]",subsection_heading,0.6,body_zone,heading_like,none,True,True
4,8,text,We performed statistical analysis using R Statistical Computing Environment version 4.0.5 (R Core Team). We assessed differences in baseline characteristics describing qualifying admissions across 3 g,"[89, 530, 793, 1023]",body_paragraph,0.6,"[""default body_paragraph for text label""]",body_paragraph,0.6,body_zone,body_like,none,True,True
4,9,paragraph_title,Results,"[92, 1068, 169, 1092]",section_heading,0.9,"[""explicit scholarly heading: Results""]",section_heading,0.9,body_zone,heading_like,canonical_section_name,True,True
4,10,text,"Of 90 773 adult admissions recorded from June 5, 2019, through December 31, 2020, our analytic data set included 60 507 admissions, comprising 49 369 unique patients, that met the inclusion criteria (","[89, 1108, 795, 1442]",body_paragraph,0.6,"[""default body_paragraph for text label""]",body_paragraph,0.6,body_zone,body_like,none,True,True
4,11,footer,JAMA Network Open. 2023;6(8):e2329729. doi:10.1001/jamanetworkopen.2023.29729,"[92, 1468, 619, 1490]",noise,0.9,"[""footer label""]",noise,0.9,,unknown_like,none,False,False
4,12,footer,"August 25, 2023","[978, 1470, 1079, 1488]",noise,0.9,"[""footer label""]",noise,0.9,body_zone,body_like,short_fragment,False,False
4,13,footer,Downloaded from jamanetwork.com by guest on 05/18/2026,"[2, 1549, 528, 1569]",noise,0.9,"[""footer label""]",noise,0.9,body_zone,body_like,none,False,False
4,14,number,4/13,"[1099, 1471, 1128, 1489]",noise,0.9,"[""page number label""]",noise,0.9,body_zone,body_like,short_fragment,False,False
5,0,header_image,,"[96, 0, 252, 19]",unknown_structural,0.2,"[""unrecognized label 'header_image'""]",unknown_structural,0.2,body_zone,unknown_like,empty,False,True
5,1,header,JAMA Network Open | Health Informatics,"[92, 68, 409, 88]",noise,0.9,"[""header label""]",noise,0.9,body_zone,body_like,none,False,False
5,2,header,"Sepsis Prediction Model vs SIRS, qSOFA, and SOFA","[813, 69, 1128, 88]",noise,0.9,"[""header label""]",noise,0.9,body_zone,body_like,none,False,False
5,3,text,"of which were diagnosed with COVID-19. Those with EHR-confirmed sepsis were older and more likely to be classified as White compared with all-cause admissions. Immunocompromised status, atheroscleroti","[89, 115, 793, 372]",body_paragraph,0.6,"[""default body_paragraph for text label""]",body_paragraph,0.6,body_zone,body_like,none,True,True
5,4,text,We observed an inverse association between an increase in PSS threshold and sensitivity and a positive association for higher PSS threshold and specificity (Table 2). For the entire length of admissio,"[89, 376, 794, 630]",body_paragraph,0.6,"[""default body_paragraph for text label""]",body_paragraph,0.6,body_zone,body_like,none,True,True
5,5,text,All the prediction scores had a median time to threshold after time zero (Table 3). SIRS criteria were positive in the largest proportion of EHR-confirmed sepsis admissions before time zero (573 [43.3,"[89, 634, 795, 841]",body_paragraph,0.6,"[""default body_paragraph for text label""]",body_paragraph,0.6,body_zone,body_like,none,True,True
5,6,image,,"[92, 894, 1085, 1400]",media_asset,0.85,"[""media label: image""]",media_asset,0.85,body_zone,body_like,empty,True,True
5,7,vision_footnote,"ASE indicates Adult Sepsis Event; BP, blood pressure; LOS, length of stay.","[91, 1423, 512, 1443]",footnote,0.7,"[""vision_footnote label: ASE indicates Adult Sepsis Event; BP, blood pressure; LOS, l""]",footnote,0.7,body_zone,body_like,none,True,True
5,8,footer,JAMA Network Open. 2023;6(8):e2329729. doi:10.1001/jamanetworkopen.2023.29729,"[92, 1468, 619, 1490]",noise,0.9,"[""footer label""]",noise,0.9,,unknown_like,none,False,False
5,9,footer,"August 25, 2023","[979, 1470, 1080, 1488]",noise,0.9,"[""footer label""]",noise,0.9,body_zone,body_like,short_fragment,False,False
5,10,footer,Downloaded from jamanetwork.com by guest on 05/18/2026,"[2, 1549, 528, 1569]",noise,0.9,"[""footer label""]",noise,0.9,body_zone,body_like,none,False,False
5,11,number,5/13,"[1099, 1471, 1128, 1489]",noise,0.9,"[""page number label""]",noise,0.9,body_zone,body_like,short_fragment,False,False
6,0,header,JAMA Network Open | Health Informatics,"[92, 66, 409, 89]",noise,0.9,"[""header label""]",noise,0.9,body_zone,body_like,none,False,False
6,1,header,"Sepsis Prediction Model vs SIRS, qSOFA, and SOFA","[813, 68, 1128, 88]",noise,0.9,"[""header label""]",noise,0.9,body_zone,body_like,none,False,False
6,2,text,"of onset of organ dysfunction by eSOFA criteria, we saw similar trends, with SIRS performing best, followed by SOFA, qSOFA, and the SPM (eTable 3 in Supplement 1). Results pertaining to additional PSS","[90, 114, 785, 191]",body_paragraph,0.6,"[""default body_paragraph for text label""]",body_paragraph,0.6,body_zone,body_like,none,True,True
6,3,paragraph_title,Discussion,"[92, 237, 200, 263]",section_heading,0.9,"[""explicit scholarly heading: Discussion""]",section_heading,0.9,body_zone,heading_like,canonical_section_name,True,True
6,4,text,"Although the SPM demonstrated better balanced accuracy and specificity for sepsis at higher-threshold PSS (8 to 10), it also missed a higher proportion of true cases and was far less timely in compari","[90, 277, 794, 407]",body_paragraph,0.6,"[""default body_paragraph for text label""]",body_paragraph,0.6,body_zone,body_like,none,True,True
6,5,figure_title,Table 1. Baseline Characteristics of Inpatient Admissions by Sepsis Category $ ^{a} $,"[93, 457, 574, 479]",table_caption,0.9,"[""table prefix matched: Table 1. Baseline Characteristics of Inpatient Admissions by""]",table_caption,0.9,display_zone,table_caption_like,table_number,True,True
6,6,table,"<table><tr><td rowspan=""2"">Characteristic</td><td colspan=""3"">Admissions, No. (%)</td></tr><tr><td>ASE sepsis (n = 1324)</td><td>COVID-19 sepsis (n = 339)</td><td>Nonsepsis (n = 58 844)</td></tr><tr><","[94, 466, 792, 1438]",media_asset,0.85,"[""media label: table""]",media_asset,0.85,body_zone,body_like,none,True,True
6,7,text,"Abbreviations: ASE, Adult Sepsis Event; ED, emergency department; ICU, intensive care unit.","[810, 1118, 1095, 1159]",body_paragraph,0.6,"[""default body_paragraph for text label""]",body_paragraph,0.6,body_zone,body_like,none,True,True
6,8,text," $ ^{a} $ $ \chi^{2} $ test was conducted for categorical variables. Kruskal-Wallis test was implemented for continuous, skewed variables.","[810, 1163, 1126, 1223]",body_paragraph,0.6,"[""default body_paragraph for text label""]",body_paragraph,0.6,body_zone,body_like,affiliation_marker,True,True
6,9,text," $ ^{b} $ Other is inclusive of categories in the electronic health record labeled as American Indian or Alaska Native, Asian, multiracial, Native Hawaiian or Other Pacific Islander, and Other (not fu","[810, 1228, 1120, 1308]",body_paragraph,0.6,"[""default body_paragraph for text label""]",body_paragraph,0.6,body_zone,body_like,affiliation_marker,True,True
6,10,text,Body mass index is calculated as weight in kilograms divided by height in meters squared.,"[810, 1313, 1125, 1353]",body_paragraph,0.6,"[""default body_paragraph for text label""]",body_paragraph,0.6,body_zone,body_like,none,True,True
6,11,vision_footnote,JAMA Network Open. 2023;6(8):e2329729. doi:10.1001/jamanetworkopen.2023.29729,"[92, 1468, 619, 1490]",footnote,0.7,"[""vision_footnote label: JAMA Network Open. 2023;6(8):e2329729. doi:10.1001/jamanetwo""]",footnote,0.7,,unknown_like,none,True,True
6,12,text," $ ^{d} $ The P value for body mass of 30 or greater was .005, and P values for all other variables were <.001.","[810, 1357, 1127, 1398]",body_paragraph,0.6,"[""default body_paragraph for text label""]",body_paragraph,0.6,body_zone,body_like,affiliation_marker,True,True
6,13,text, $ ^{e} $ Immunocompromised indicates patient with HIV/AIDS or metastatic cancer.,"[810, 1403, 1083, 1444]",body_paragraph,0.6,"[""default body_paragraph for text label""]",body_paragraph,0.6,body_zone,body_like,affiliation_marker,True,True
6,14,footer,"August 25, 2023","[978, 1470, 1079, 1488]",noise,0.9,"[""footer label""]",noise,0.9,body_zone,body_like,short_fragment,False,False
6,15,footer,Downloaded from jamanetwork.com by guest on 05/18/2026,"[2, 1549, 528, 1570]",noise,0.9,"[""footer label""]",noise,0.9,body_zone,body_like,none,False,False
6,16,number,6/13,"[1099, 1471, 1128, 1489]",noise,0.9,"[""page number label""]",noise,0.9,body_zone,body_like,short_fragment,False,False
7,0,header_image,,"[95, 0, 252, 19]",unknown_structural,0.2,"[""unrecognized label 'header_image'""]",unknown_structural,0.2,body_zone,unknown_like,empty,False,True
7,1,header,JAMA Network Open | Health Informatics,"[93, 67, 409, 89]",noise,0.9,"[""header label""]",noise,0.9,body_zone,body_like,none,False,False
7,2,header,"Sepsis Prediction Model vs SIRS, qSOFA, and SOFA","[813, 69, 1128, 89]",noise,0.9,"[""header label""]",noise,0.9,body_zone,body_like,none,False,False
7,3,text,with increased score complexity and lack of transparency of the SPM epitomizes its major flaw: it appears to predict sepsis long after the clinician has recognized possible sepsis and acted on that su,"[90, 114, 793, 214]",body_paragraph,0.6,"[""default body_paragraph for text label""]",body_paragraph,0.6,body_zone,body_like,none,True,True
7,4,text,"At higher PSS scores (8 to 10), only 12.9% to 19.7% of patients could have been identified by the SPM in a clinically relevant time prior to clinician action (Table 3). In addition to poor timeliness ","[90, 218, 792, 398]",body_paragraph,0.6,"[""default body_paragraph for text label""]",body_paragraph,0.6,body_zone,body_like,none,True,True
7,5,text,"Given the existing observational data regarding the importance of early antimicrobial administration, the patients who would benefit most from early recognition, resource allocation, and appropriate a","[90, 401, 796, 580]",body_paragraph,0.6,"[""default body_paragraph for text label""]",body_paragraph,0.6,body_zone,body_like,none,True,True
7,6,figure_title,Table 2. Performance Metrics for Classification of Admission (All Sepsis vs Nonsepsis),"[92, 615, 630, 635]",table_caption,0.9,"[""table prefix matched: Table 2. Performance Metrics for Classification of Admission""]",table_caption,0.9,display_zone,table_caption_like,table_number,True,True
7,7,table,"<table><tr><td rowspan=""2"">All sepsis classification (n = 1663)</td><td colspan=""7"">Performance metric (95% CI)</td></tr><tr><td>Accuracy</td><td>Sensitivity</td><td>Specificity</td><td>False negative","[94, 641, 1124, 911]",media_asset,0.85,"[""media label: table""]",media_asset,0.85,body_zone,body_like,none,True,True
7,8,vision_footnote,"Abbreviations: PSS, Predicting Sepsis Score; qSOFA, quick Sepsis-Related Organ Failure Assessment; SIRS, Systemic Inflammatory Response Syndrome; SOFA, Sequential Organ Failure Assessment.","[92, 919, 600, 978]",footnote,0.7,"[""vision_footnote label: Abbreviations: PSS, Predicting Sepsis Score; qSOFA, quick Se""]",footnote,0.7,body_zone,body_like,none,True,True
7,9,vision_footnote, $ ^{c} $ Balanced accuracy is calculated as sensitivity plus specificity divided by 2.,"[618, 919, 1051, 938]",footnote,0.7,"[""vision_footnote label: $ ^{c} $ Balanced accuracy is calculated as sensitivity plus""]",footnote,0.7,body_zone,body_like,affiliation_marker,True,True
7,10,vision_footnote, $ ^{a} $ False negative indicates the proportion of missed true sepsis cases.,"[92, 984, 490, 1003]",footnote,0.7,"[""vision_footnote label: $ ^{a} $ False negative indicates the proportion of missed t""]",footnote,0.7,body_zone,body_like,affiliation_marker,True,True
7,11,vision_footnote, $ ^{d} $ Diagnostic odds ratio is calculated as the positive likelihood ratio divided by the negative likelihood ratio.,"[618, 944, 1084, 984]",footnote,0.7,"[""vision_footnote label: $ ^{d} $ Diagnostic odds ratio is calculated as the positive""]",footnote,0.7,body_zone,body_like,affiliation_marker,True,True
7,12,vision_footnote, $ ^{b} $ False positive indicates the proportion of nonsepsis cases falsely classified as sepsis.,"[92, 1009, 584, 1029]",footnote,0.7,"[""vision_footnote label: $ ^{b} $ False positive indicates the proportion of nonsepsi""]",footnote,0.7,body_zone,body_like,affiliation_marker,True,True
7,13,figure_title,Table 3. Performance Metrics for Admissions With Respect to Time Zero Among 1324 Electronic Health Record-Confirmed Sepsis Admissions $ ^{a} $,"[92, 1075, 740, 1115]",table_caption,0.9,"[""table prefix matched: Table 3. Performance Metrics for Admissions With Respect to ""]",table_caption,0.9,display_zone,table_caption_like,table_number,True,True
7,14,table,"<table><tr><td rowspan=""2"">Performance metric</td><td colspan=""4"">Diagnostic criteria, No. (%)</td></tr><tr><td>PSS</td><td>SIRS</td><td>qSOFA</td><td>SOFA</td></tr><tr><td>Difference between time of ","[94, 1119, 790, 1279]",media_asset,0.85,"[""media label: table""]",media_asset,0.85,body_zone,body_like,none,True,True
7,15,vision_footnote,"Abbreviations: PSS, Predicting Sepsis Score; qSOFA, quick Sepsis-Related Organ Failure Assessment; SIRS, Systemic Inflammatory Response Syndrome; SOFA, Sequential Organ Failure Assessment.","[91, 1287, 755, 1326]",footnote,0.7,"[""vision_footnote label: Abbreviations: PSS, Predicting Sepsis Score; qSOFA, quick Se""]",footnote,0.7,body_zone,body_like,none,True,True
7,16,vision_footnote, $ ^{a} $ Sepsis is defined as meeting Adult Sepsis Event criteria.,"[92, 1333, 423, 1353]",footnote,0.7,"[""vision_footnote label: $ ^{a} $ Sepsis is defined as meeting Adult Sepsis Event cri""]",footnote,0.7,body_zone,body_like,affiliation_marker,True,True
7,17,vision_footnote,"Time zero indicates 15 minutes before clinician action (blood culture or antimicrobial order). Positive and negative values indicate threshold being met before and after time zero, respectively. Diffe","[91, 1357, 792, 1416]",footnote,0.7,"[""vision_footnote label: Time zero indicates 15 minutes before clinician action (bloo""]",footnote,0.7,body_zone,body_like,none,True,True
7,18,vision_footnote,"Threshold score for PSS was 8 or greater; SIRS, 2 or greater; qSOFA, 2 or greater; SOFA, 2 or greater.","[92, 1423, 674, 1443]",footnote,0.7,"[""vision_footnote label: Threshold score for PSS was 8 or greater; SIRS, 2 or greater""]",footnote,0.7,body_zone,body_like,none,True,True
7,19,footer,JAMA Network Open. 2023;6(8):e2329729. doi:10.1001/jamanetworkopen.2023.29729,"[93, 1468, 618, 1490]",noise,0.9,"[""footer label""]",noise,0.9,,unknown_like,none,False,False
7,20,footer,"August 25, 2023","[979, 1470, 1081, 1489]",noise,0.9,"[""footer label""]",noise,0.9,body_zone,body_like,short_fragment,False,False
7,21,footer,Downloaded from jamanetwork.com by guest on 05/18/2026,"[2, 1550, 527, 1569]",noise,0.9,"[""footer label""]",noise,0.9,body_zone,body_like,none,False,False
7,22,number,7/13,"[1100, 1471, 1128, 1489]",noise,0.9,"[""page number label""]",noise,0.9,body_zone,body_like,short_fragment,False,False
8,0,header,JAMA Network Open | Health Informatics,"[92, 65, 409, 89]",noise,0.9,"[""header label""]",noise,0.9,body_zone,body_like,none,False,False
8,1,header,"Sepsis Prediction Model vs SIRS, qSOFA, and SOFA","[813, 67, 1128, 89]",noise,0.9,"[""header label""]",noise,0.9,body_zone,body_like,none,False,False
8,2,chart,,"[105, 134, 967, 1443]",media_asset,0.85,"[""media label: chart""]",media_asset,0.85,body_zone,body_like,empty,True,True
8,3,vision_footnote,JAMA Network Open. 2023;6(8):e2329729. doi:10.1001/jamanetworkopen.2023.29729,"[92, 1468, 619, 1490]",footnote,0.7,"[""vision_footnote label: JAMA Network Open. 2023;6(8):e2329729. doi:10.1001/jamanetwo""]",footnote,0.7,,unknown_like,none,True,True
8,4,vision_footnote,This figure demonstrates the difference between time zero and time of sepsis threshold positivity. They are negative and are shaded in blue. Those admissions with a threshold score after time 15 minut,"[984, 136, 1062, 1442]",footnote,0.7,"[""vision_footnote label: This figure demonstrates the difference between time zero an""]",footnote,0.7,body_zone,body_like,none,True,True
8,5,footer,"August 25, 2023","[978, 1470, 1079, 1488]",noise,0.9,"[""footer label""]",noise,0.9,body_zone,body_like,short_fragment,False,False
8,6,footer,Downloaded from jamanetwork.com by guest on 05/18/2026,"[2, 1549, 527, 1569]",noise,0.9,"[""footer label""]",noise,0.9,body_zone,body_like,none,False,False
8,7,number,8/13,"[1099, 1470, 1127, 1489]",noise,0.9,"[""page number label""]",noise,0.9,body_zone,body_like,short_fragment,False,False
9,0,header,JAMA Network Open | Health Informatics,"[93, 67, 410, 89]",noise,0.9,"[""header label""]",noise,0.9,body_zone,body_like,none,False,False
9,1,header,"Sepsis Prediction Model vs SIRS, qSOFA, and SOFA","[813, 69, 1128, 89]",noise,0.9,"[""header label""]",noise,0.9,body_zone,body_like,none,False,False
9,2,text,existing limitations to timeliness that may be due to other aspects of model development in order to meaningfully impact sepsis recognition or management.,"[90, 115, 790, 164]",body_paragraph,0.6,"[""default body_paragraph for text label""]",body_paragraph,0.6,body_zone,body_like,none,True,True
9,3,paragraph_title,Strengths and Limitations,"[91, 193, 311, 215]",subsection_heading,0.6,"[""unnumbered paragraph_title, inferred level subsection_heading: Strengths and Limitations""]",subsection_heading,0.6,body_zone,heading_like,none,True,True
9,4,text,The strengths of our study include a large sample size and inclusion of patients from both community-based and tertiary referral hospitals. Utilization of the CDC ASE criteria as well as separately de,"[89, 219, 795, 501]",body_paragraph,0.6,"[""default body_paragraph for text label""]",body_paragraph,0.6,body_zone,body_like,none,True,True
9,5,text,"This study also has limitations, including investigation of only a single health system, the observational study design, the inherent heterogeneity of sepsis, reliance on EHR data, and missing data. B","[89, 504, 795, 763]",body_paragraph,0.6,"[""default body_paragraph for text label""]",body_paragraph,0.6,body_zone,body_like,none,True,True
9,6,paragraph_title,Conclusions,"[92, 808, 213, 833]",section_heading,0.9,"[""explicit scholarly heading: Conclusions""]",section_heading,0.9,body_zone,heading_like,canonical_section_name,True,True
9,7,text,"In this cohort study of 60 507 hospital admissions, we found that although the SPM marginally outperformed existing prediction scores in balanced accuracy for classification of sepsis, it suffers from","[90, 849, 796, 1107]",body_paragraph,0.6,"[""default body_paragraph for text label""]",body_paragraph,0.6,body_zone,body_like,none,True,True
9,8,text,ARTICLE INFORMATION,"[91, 1176, 256, 1196]",unknown_structural,0.3,"[""short text, uncertain role""]",unknown_structural,0.3,body_zone,body_like,short_fragment,False,True
9,9,text,"Accepted for Publication: July 7, 2023.","[91, 1198, 342, 1220]",body_paragraph,0.6,"[""default body_paragraph for text label""]",body_paragraph,0.6,body_zone,body_like,none,True,True
9,10,footnote,"Published: August 25, 2023. doi:10.1001/jamanetworkopen.2023.29729","[90, 1226, 545, 1248]",footnote,0.7,"[""footnote label: Published: August 25, 2023. doi:10.1001/jamanetworkopen.2023""]",footnote,0.7,,unknown_like,none,True,True
9,11,footnote,Open Access: This is an open access article distributed under the terms of the CC-BY License. © 2023 Schertz AR et al. JAMA Network Open.,"[90, 1254, 788, 1297]",footnote,0.7,"[""footnote label: Open Access: This is an open access article distributed unde""]",footnote,0.7,body_zone,body_like,none,True,True
9,12,footnote,"Corresponding Author: Adam R. Schertz, MD, MS, Section of Pulmonology, Critical Care, Allergy & Immunologic Diseases, Atrium Health Wake Forest Baptist, One Medical Center Boulevard, Winston-Salem, NC","[90, 1303, 819, 1370]",footnote,0.7,"[""footnote label: Corresponding Author: Adam R. Schertz, MD, MS, Section of Pu""]",footnote,0.7,body_zone,body_like,none,True,True
9,13,footnote,"Author Affiliations: Department of Internal Medicine, Atrium Health Wake Forest Baptist, Winston-Salem, North Carolina (Schertz, Bertoni, Thomas); Section of Pulmonology, Critical Care, Allergy and Im","[91, 1376, 797, 1443]",footnote,0.7,"[""footnote label: Author Affiliations: Department of Internal Medicine, Atrium""]",footnote,0.7,body_zone,body_like,none,True,True
9,14,footer,JAMA Network Open. 2023;6(8):e2329729. doi:10.1001/jamanetworkopen.2023.29729,"[92, 1468, 619, 1490]",noise,0.9,"[""footer label""]",noise,0.9,,unknown_like,none,False,False
9,15,footer,"August 25, 2023","[977, 1470, 1079, 1488]",noise,0.9,"[""footer label""]",noise,0.9,body_zone,body_like,short_fragment,False,False
9,16,footer,Downloaded from jamanetwork.com by guest on 05/18/2026,"[2, 1549, 528, 1570]",noise,0.9,"[""footer label""]",noise,0.9,body_zone,body_like,none,False,False
9,17,number,9/13,"[1098, 1470, 1128, 1489]",noise,0.9,"[""page number label""]",noise,0.9,body_zone,body_like,short_fragment,False,False
10,0,header,JAMA Network Open | Health Informatics,"[93, 67, 409, 88]",noise,0.9,"[""header label""]",noise,0.9,body_zone,body_like,none,False,False
10,1,header,"Sepsis Prediction Model vs SIRS, qSOFA, and SOFA","[813, 69, 1128, 88]",noise,0.9,"[""header label""]",noise,0.9,body_zone,body_like,none,False,False
10,2,text,"and Data Science, Division of Public Health Science, Atrium Health Wake Forest Baptist, Winston-Salem, North Carolina (Lenoir, Bertoni); Social Sciences and Health Policy, Wake Forest University Schoo","[91, 116, 786, 202]",body_paragraph,0.6,"[""default body_paragraph for text label""]",body_paragraph,0.6,body_zone,body_like,none,True,True
10,3,text,Author Contributions: Dr Schertz and Ms Lenoir had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.,"[90, 210, 720, 251]",frontmatter_noise,0.88,"[""default body_paragraph for text label"", ""late role resolution: editorial phrase cross-validates non-body classification"", ""zone=body_zone"", ""style_family=support_like""]",body_paragraph,0.6,body_zone,support_like,none,False,False
10,4,text,"Concept and design: Schertz, Bertoni, Levine, Mongraw-Chaffin, Thomas.","[91, 259, 544, 279]",body_paragraph,0.6,"[""default body_paragraph for text label""]",body_paragraph,0.6,body_zone,body_like,none,True,True
10,5,text,"Acquisition, analysis, or interpretation of data: Schertz, Lenoir, Bertoni, Levine, Mongraw-Chaffin.","[91, 287, 687, 306]",body_paragraph,0.6,"[""default body_paragraph for text label""]",body_paragraph,0.6,body_zone,body_like,none,True,True
10,6,text,"Drafting of the manuscript: Schertz, Lenoir, Thomas.","[92, 315, 416, 335]",body_paragraph,0.6,"[""default body_paragraph for text label""]",body_paragraph,0.6,body_zone,body_like,none,True,True
10,7,text,"Critical review of the manuscript for important intellectual content: Schertz, Lenoir, Bertoni, Levine, Mongraw-Chaffin.","[91, 344, 764, 383]",body_paragraph,0.6,"[""default body_paragraph for text label""]",body_paragraph,0.6,body_zone,body_like,none,True,True
10,8,text,"Statistical analysis: Lenoir, Levine, Mongraw-Chaffin.","[92, 393, 419, 412]",body_paragraph,0.6,"[""default body_paragraph for text label""]",body_paragraph,0.6,body_zone,body_like,none,True,True
10,9,text,Obtained funding: Schertz.,"[92, 421, 261, 440]",body_paragraph,0.6,"[""default body_paragraph for text label""]",body_paragraph,0.6,body_zone,body_like,none,True,True
10,10,text,"Administrative, technical, or material support: Bertoni.","[91, 449, 427, 468]",body_paragraph,0.6,"[""default body_paragraph for text label""]",body_paragraph,0.6,body_zone,body_like,none,True,True
10,11,text,"Supervision: Schertz, Thomas.","[92, 477, 281, 495]",body_paragraph,0.6,"[""default body_paragraph for text label""]",body_paragraph,0.6,body_zone,body_like,none,True,True
10,12,text,Conflict of Interest Disclosures: None reported.,"[92, 505, 397, 523]",frontmatter_noise,0.6,"[""default body_paragraph for text label"", ""frontmatter_side_zone excluded from body flow""]",frontmatter_noise,0.6,frontmatter_side_zone,support_like,none,False,False
10,13,text,Funding/Support: This project was funded by the Wake Forest Clinical and Translational Science Institute.,"[91, 533, 743, 552]",body_paragraph,0.6,"[""default body_paragraph for text label""]",body_paragraph,0.6,body_zone,body_like,none,True,True
10,14,text,"Role of the Funder/Sponsor: The funding source approved the design and conduct of the study, but had no role in the collection, management, analysis, and interpretation of the data; preparation, revie","[92, 561, 783, 624]",body_paragraph,0.6,"[""default body_paragraph for text label""]",body_paragraph,0.6,body_zone,body_like,none,True,True
10,15,paragraph_title,Data Sharing Statement: See Supplement 2.,"[92, 633, 372, 652]",subsection_heading,0.6,"[""unnumbered paragraph_title, inferred level subsection_heading: Data Sharing Statement: See Supplement 2.""]",subsection_heading,0.6,body_zone,body_like,none,True,True
10,16,text,"Additional Contributions: We would like to thank the following people for their contributions to this work. Brian Wells, MD, PhD (Department of Family Medicine, Atrium Health Wake Forest Baptist), pro","[92, 661, 794, 857]",body_paragraph,0.6,"[""default body_paragraph for text label""]",body_paragraph,0.6,body_zone,body_like,none,True,True
10,17,paragraph_title,REFERENCES,"[93, 875, 186, 893]",reference_heading,0.9,"[""references heading: REFERENCES""]",reference_heading,0.9,reference_zone,unknown_like,short_fragment,True,True
10,18,text,"1. Singer M, Deutschman CS, Seymour CW, et al. The third international consensus definitions for sepsis and septic shock (Sepsis-3). JAMA. 2016;315(8):801-810. doi:10.1001/jama.2016.0287","[92, 898, 794, 940]",reference_item,0.6,"[""reference-like pattern: 1. Singer M, Deutschman CS, Seymour CW, et al. The third int""]",reference_item,0.6,reference_zone,reference_like,reference_numeric_dot,True,True
10,19,text,"2. Rhee C, Dantes R, Epstein L, et al; CDC Prevention Epicenter Program. Incidence and trends of sepsis in US hospitals using clinical vs claims data, 2009-2014. JAMA. 2017;318(13):1241-1249. doi:10.1","[92, 948, 772, 990]",reference_item,0.6,"[""reference-like pattern: 2. Rhee C, Dantes R, Epstein L, et al; CDC Prevention Epicen""]",reference_item,0.6,reference_zone,reference_like,reference_numeric_dot,True,True
10,20,text,"3. Torio CM, Moore BJ. National inpatient hospital costs: the most expensive conditions by payer, 2013. HCUP Statistical Brief #204. May 2016. Accessed July 17, 2023. https://hcup-us.ahrq.gov/reports/","[92, 998, 794, 1061]",reference_item,0.6,"[""reference-like pattern: 3. Torio CM, Moore BJ. National inpatient hospital costs: th""]",reference_item,0.6,reference_zone,reference_like,reference_numeric_dot,True,True
10,21,text,"4. Rhee C, Jones TM, Hamad Y, et al; Centers for Disease Control and Prevention (CDC) Prevention Epicenters Program. Prevalence, underlying causes, and preventability of sepsis-associated mortality in","[92, 1069, 770, 1134]",reference_item,0.6,"[""reference-like pattern: 4. Rhee C, Jones TM, Hamad Y, et al; Centers for Disease Con""]",reference_item,0.6,reference_zone,reference_like,reference_numeric_dot,True,True
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10,23,text,"5. Prescott HC, Angus DC. Enhancing recovery from sepsis: a review. JAMA. 2018;319(1):62-75. doi:10.1001/jama.2017.17687","[92, 1141, 786, 1181]",reference_item,0.6,"[""reference-like pattern: 5. Prescott HC, Angus DC. Enhancing recovery from sepsis: a ""]",reference_item,0.6,reference_zone,reference_like,reference_numeric_dot,True,True
10,24,text,"7. Fohner AE, Greene JD, Lawson BL, et al. Assessing clinical heterogeneity in sepsis through treatment patterns and machine learning. J Am Med Inform Assoc. 2019;26(12):1466-1477. doi:10.1093/jamia/o","[92, 1263, 784, 1304]",reference_item,0.6,"[""reference-like pattern: 7. Fohner AE, Greene JD, Lawson BL, et al. Assessing clinica""]",reference_item,0.6,reference_zone,reference_like,reference_numeric_dot,True,True
10,25,text,"8. Iskander KN, Osuchowski MF, Stearns-Kurosawa DJ, et al. Sepsis: multiple abnormalities, heterogeneous responses, and evolving understanding. Physiol Rev. 2013;93(3):1247-1288. doi:10.1152/physrev.0","[93, 1313, 761, 1355]",reference_item,0.6,"[""reference-like pattern: 8. Iskander KN, Osuchowski MF, Stearns-Kurosawa DJ, et al. S""]",reference_item,0.6,reference_zone,reference_like,reference_numeric_dot,True,True
10,26,text,"9. Seymour CW, Gesten F, Prescott HC, et al. Time to treatment and mortality during mandated emergency care for sepsis. N Engl J Med. 2017;376(23):2235-2244. doi:10.1056/NEJMoa1703058","[91, 1363, 782, 1405]",reference_item,0.6,"[""reference-like pattern: 9. Seymour CW, Gesten F, Prescott HC, et al. Time to treatme""]",reference_item,0.6,reference_zone,reference_like,reference_numeric_dot,True,True
10,27,footer,JAMA Network Open. 2023;6(8):e2329729. doi:10.1001/jamanetworkopen.2023.29729,"[92, 1469, 618, 1490]",noise,0.9,"[""footer label""]",noise,0.9,,unknown_like,none,False,False
10,28,footer,"August 25, 2023","[972, 1470, 1073, 1489]",noise,0.9,"[""footer label""]",noise,0.9,tail_nonref_hold_zone,unknown_like,short_fragment,False,False
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11,0,header,JAMA Network Open | Health Informatics,"[93, 68, 408, 88]",noise,0.9,"[""header label""]",noise,0.9,,unknown_like,none,False,False
11,1,header,"Sepsis Prediction Model vs SIRS, qSOFA, and SOFA","[813, 69, 1128, 88]",noise,0.9,"[""header label""]",noise,0.9,,unknown_like,none,False,False
11,2,reference_content,"10. Weinberger J, Rhee C, Klompas M. A critical analysis of the literature on time-to-antibiotics in suspected sepsis. J Infect Dis. 2020;222(suppl 2):S110-S118. doi:10.1093/infdis/jiaa146","[91, 116, 792, 158]",reference_item,0.85,"[""reference content label: 10. Weinberger J, Rhee C, Klompas M. A critical analysis of ""]",reference_item,0.85,reference_zone,reference_like,reference_numeric_dot,True,True
11,3,reference_content,"11. Pruinelli L, Westra BL, Yadav P, et al. Delay within the 3-hour surviving sepsis campaign guideline on mortality for patients with severe sepsis and septic shock. Crit Care Med. 2018;46(4):500-505","[92, 165, 783, 227]",reference_item,0.85,"[""reference content label: 11. Pruinelli L, Westra BL, Yadav P, et al. Delay within the""]",reference_item,0.85,reference_zone,reference_like,reference_numeric_dot,True,True
11,4,reference_content,"12. Andersson M, Östholm-Balkhed Å, Fredrikson M, et al. Delay of appropriate antibiotic treatment is associated with high mortality in patients with community-onset sepsis in a Swedish setting. Eur J","[92, 237, 786, 300]",reference_item,0.85,"[""reference content label: 12. Andersson M, \u00d6stholm-Balkhed \u00c5, Fredrikson M, et al. Del""]",reference_item,0.85,reference_zone,reference_like,reference_numeric_dot,True,True
11,5,reference_content,"13. Peltan ID, Brown SM, Bledsoe JR, et al. ED door-to-antibiotic time and long-term mortality in sepsis. Chest. 2019;155(5):938-946. doi:10.1016/j.chest.2019.02.008","[92, 309, 768, 350]",reference_item,0.85,"[""reference content label: 13. Peltan ID, Brown SM, Bledsoe JR, et al. ED door-to-antib""]",reference_item,0.85,reference_zone,reference_like,reference_numeric_dot,True,True
11,6,reference_content,"14. Whiles BB, Deis AS, Simpson SQ. Increased time to initial antimicrobial administration is associated with progression to septic shock in severe sepsis patients. Crit Care Med. 2017;45(4):623-629. ","[92, 359, 751, 420]",reference_item,0.85,"[""reference content label: 14. Whiles BB, Deis AS, Simpson SQ. Increased time to initia""]",reference_item,0.85,reference_zone,reference_like,reference_numeric_dot,True,True
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11,8,reference_content,"16. Seymour CW, Kahn JM, Martin-Gill C, et al. Delays from first medical contact to antibiotic administration for sepsis. Crit Care Med. 2017;45(5):759-765. doi:10.1097/CCM.0000000000002264","[92, 503, 775, 544]",reference_item,0.85,"[""reference content label: 16. Seymour CW, Kahn JM, Martin-Gill C, et al. Delays from f""]",reference_item,0.85,reference_zone,reference_like,reference_numeric_dot,True,True
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11,21,footer,JAMA Network Open. 2023;6(8):e2329729. doi:10.1001/jamanetworkopen.2023.29729,"[91, 1468, 618, 1490]",noise,0.9,"[""footer label""]",noise,0.9,,unknown_like,none,False,False
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12,0,header,JAMA Network Open | Health Informatics,"[93, 68, 408, 88]",noise,0.9,"[""header label""]",noise,0.9,,unknown_like,none,False,False
12,1,header,"Sepsis Prediction Model vs SIRS, qSOFA, and SOFA","[813, 69, 1128, 88]",noise,0.9,"[""header label""]",noise,0.9,,unknown_like,none,False,False
12,2,text,"29. Evans L, Rhodes A, Alhazzani W, et al. Executive summary: Surviving Sepsis Campaign: international guidelines for the management of sepsis and septic shock 2021. Crit Care Med. 2021;49(11):1974-19","[92, 116, 763, 179]",reference_item,0.6,"[""reference-like pattern: 29. Evans L, Rhodes A, Alhazzani W, et al. Executive summary""]",reference_item,0.6,reference_zone,reference_like,reference_numeric_dot,True,True
12,3,text,"30. Fernando SM, Tran A, Taljaard M, et al. Prognostic accuracy of the quick Sequential Organ Failure Assessment for mortality in patients with suspected infection: a systematic review and meta-analys","[92, 188, 788, 250]",reference_item,0.6,"[""reference-like pattern: 30. Fernando SM, Tran A, Taljaard M, et al. Prognostic accur""]",reference_item,0.6,reference_zone,reference_like,reference_numeric_dot,True,True
12,4,text,"31. Wong A, Otles E, Donnelly JP, et al. External validation of a widely implemented proprietary sepsis prediction model in hospitalized patients. JAMA Intern Med. 2021;181(8):1065-1070. doi:10.1001/j","[92, 259, 785, 300]",reference_item,0.6,"[""reference-like pattern: 31. Wong A, Otles E, Donnelly JP, et al. External validation""]",reference_item,0.6,reference_zone,reference_like,reference_numeric_dot,True,True
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12,6,text,"33. Habib AR, Lin AL, Grant RW. The epic sepsis model falls short—the importance of external validation. JAMA Intern Med. 2021;181(8):1040-1041. doi:10.1001/jamainternmed.2021.3333","[92, 359, 772, 400]",reference_item,0.6,"[""reference-like pattern: 33. Habib AR, Lin AL, Grant RW. The epic sepsis model falls ""]",reference_item,0.6,reference_zone,reference_like,reference_numeric_dot,True,True
12,7,text,"34. von Elm E, Altman DG, Egger M, Pocock SJ, Gøtzsche PC, Vandenbroucke JP; STROBE Initiative. The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement: guidelines ","[92, 409, 749, 472]",reference_item,0.6,"[""reference-like pattern: 34. von Elm E, Altman DG, Egger M, Pocock SJ, G\u00f8tzsche PC, V""]",reference_item,0.6,reference_zone,reference_like,reference_numeric_dot,True,True
12,8,text,"35. Rhee C, Zhang Z, Kadri SS, et al; CDC Prevention Epicenters Program. Sepsis surveillance using adult sepsis events simplified eSOFA criteria versus Sepsis-3 Sequential Organ Failure Assessment cri","[92, 481, 793, 543]",reference_item,0.6,"[""reference-like pattern: 35. Rhee C, Zhang Z, Kadri SS, et al; CDC Prevention Epicent""]",reference_item,0.6,reference_zone,reference_like,reference_numeric_dot,True,True
12,9,text,"36. Kluberg SA, Hou L, Dutcher SK, et al. Validation of diagnosis codes to identify hospitalized COVID-19 patients in health care claims data. Pharmacoepidemiol Drug Saf. 2022;31(4):476-480. doi:10.10","[91, 553, 787, 595]",reference_item,0.6,"[""reference-like pattern: 36. Kluberg SA, Hou L, Dutcher SK, et al. Validation of diag""]",reference_item,0.6,reference_zone,reference_like,reference_numeric_dot,True,True
12,10,text,"37. Quan H, Li B, Couris CM, et al. Updating and validating the Charlson comorbidity index and score for risk adjustment in hospital discharge abstracts using data from 6 countries. Am J Epidemiol. 20","[92, 603, 789, 665]",reference_item,0.6,"[""reference-like pattern: 37. Quan H, Li B, Couris CM, et al. Updating and validating ""]",reference_item,0.6,reference_zone,reference_like,reference_numeric_dot,True,True
12,11,text,"38. Sloan SNB, Rodriguez N, Seward T, et al. Compliance with SEP-1 guidelines is associated with improved outcomes for septic shock but not for severe sepsis. J Intensive Med. 2022;2(3):167-172. doi:1","[92, 674, 758, 736]",reference_item,0.6,"[""reference-like pattern: 38. Sloan SNB, Rodriguez N, Seward T, et al. Compliance with""]",reference_item,0.6,reference_zone,reference_like,reference_numeric_dot,True,True
12,12,text,"39. Glas AS, Lijmer JG, Prins MH, Bonsel GJ, Bossuyt PM. The diagnostic odds ratio: a single indicator of test performance. J Clin Epidemiol. 2003;56(11):1129-1135. doi:10.1016/S0895-4356(03)00177-X","[91, 746, 755, 787]",reference_item,0.6,"[""reference-like pattern: 39. Glas AS, Lijmer JG, Prins MH, Bonsel GJ, Bossuyt PM. The""]",reference_item,0.6,reference_zone,reference_like,reference_numeric_dot,True,True
12,13,text,"40. Diaz N. Epic overhauls sepsis algorithm. October 6, 2022. Accessed June 20, 2023. https://www.beckershospitalreview.com/ehrs/epic-overhauls-sepsis-algorithm.html","[92, 796, 716, 837]",reference_item,0.6,"[""reference-like pattern: 40. Diaz N. Epic overhauls sepsis algorithm. October 6, 2022""]",reference_item,0.6,reference_zone,reference_like,reference_numeric_dot,True,True
12,14,text,"41. Rhee C, Wang R, Zhang Z, Fram D, Kadri SS, Klompas M; CDC Prevention Epicenters Program. Epidemiology of hospital-onset versus community-onset sepsis in U.S. hospitals and association with mortali","[92, 846, 790, 929]",reference_item,0.6,"[""reference-like pattern: 41. Rhee C, Wang R, Zhang Z, Fram D, Kadri SS, Klompas M; CD""]",reference_item,0.6,reference_zone,reference_like,reference_numeric_dot,True,True
12,15,text,"42. Baghdadi JD, Brook RH, Uslan DZ, et al. Association of a care bundle for early sepsis management with mortality among patients with hospital-onset or community-onset sepsis. JAMA Intern Med. 2020;","[92, 940, 746, 1003]",reference_item,0.6,"[""reference-like pattern: 42. Baghdadi JD, Brook RH, Uslan DZ, et al. Association of a""]",reference_item,0.6,reference_zone,reference_like,reference_numeric_dot,True,True
12,16,text,"43. Baghdadi JD, Wong MD, Uslan DZ, et al. Adherence to the SEP-1 sepsis bundle in hospital-onset v. community-onset sepsis: a multicenter retrospective cohort study. J Gen Intern Med. 2020;35(4):1153","[92, 1012, 791, 1074]",reference_item,0.6,"[""reference-like pattern: 43. Baghdadi JD, Wong MD, Uslan DZ, et al. Adherence to the ""]",reference_item,0.6,reference_zone,reference_like,reference_numeric_dot,True,True
12,17,text,"44. Page B, Klompas M, Chan C, et al. Surveillance for healthcare-associated infections: hospital-onset adult sepsis events versus current reportable conditions. Clin Infect Dis. 2021;73(6):1013-1019.","[92, 1084, 781, 1125]",reference_item,0.6,"[""reference-like pattern: 44. Page B, Klompas M, Chan C, et al. Surveillance for healt""]",reference_item,0.6,reference_zone,reference_like,reference_numeric_dot,True,True
12,18,text,"45. Rhee C, Klompas M. Should hospital-onset adult sepsis event surveillance be routine... or even mandatory? Antimicrob Steward Healthc Epidemiol. 2022;2(1):e32. doi:10.1017/ash.2022.16","[92, 1133, 779, 1175]",reference_item,0.6,"[""reference-like pattern: 45. Rhee C, Klompas M. Should hospital-onset adult sepsis ev""]",reference_item,0.6,reference_zone,reference_like,reference_numeric_dot,True,True
12,19,text,"46. Cárdenas-Turanzas M, Ensor J, Wakefield C, et al. Cross-validation of a Sequential Organ Failure Assessment score-based model to predict mortality in patients with cancer admitted to the intensive","[92, 1183, 778, 1246]",reference_item,0.6,"[""reference-like pattern: 46. C\u00e1rdenas-Turanzas M, Ensor J, Wakefield C, et al. Cross-""]",reference_item,0.6,reference_zone,reference_like,reference_numeric_dot,True,True
12,20,paragraph_title,SUPPLEMENT 1.,"[92, 1272, 202, 1290]",unknown_structural,0.6,"[""unnumbered paragraph_title, inferred level section_heading: SUPPLEMENT 1.""]",section_heading,0.6,tail_nonref_hold_zone,unknown_like,short_fragment,False,True
12,21,text,"eFigure 1. Diagnostic Odds Ratios for Sepsis, by Model","[92, 1294, 431, 1314]",body_paragraph,0.6,"[""default body_paragraph for text label""]",body_paragraph,0.6,tail_nonref_hold_zone,unknown_like,none,True,True
12,22,text,eFigure 2. Kaplan-Meier Analysis of Time to Threshold Positivity vs Time Zero,"[93, 1317, 573, 1335]",body_paragraph,0.6,"[""default body_paragraph for text label""]",body_paragraph,0.6,tail_nonref_hold_zone,unknown_like,none,True,True
12,23,text,"eFigure 3. Plot of Criteria Time With Respect to Time Zero for Each EHR-Confirmed Admission, PSS Only","[92, 1339, 732, 1358]",body_paragraph,0.6,"[""default body_paragraph for text label""]",body_paragraph,0.6,tail_nonref_hold_zone,unknown_like,none,True,True
12,24,text,eTable 1. Missing Data Stratified by Admission Type,"[93, 1361, 414, 1380]",body_paragraph,0.6,"[""default body_paragraph for text label""]",body_paragraph,0.6,tail_nonref_hold_zone,unknown_like,none,True,True
12,25,text,eTable 2. Additional Performance Metrics for Classification of Admission,"[92, 1383, 540, 1402]",body_paragraph,0.6,"[""default body_paragraph for text label""]",body_paragraph,0.6,tail_nonref_hold_zone,unknown_like,none,True,True
12,26,footer,JAMA Network Open. 2023;6(8):e2329729. doi:10.1001/jamanetworkopen.2023.29729,"[92, 1469, 618, 1489]",noise,0.9,"[""footer label""]",noise,0.9,,unknown_like,none,False,False
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13,1,header,JAMA Network Open | Health Informatics,"[92, 67, 409, 89]",noise,0.9,"[""header label""]",noise,0.9,,unknown_like,none,False,False
13,2,header,"Sepsis Prediction Model vs SIRS, qSOFA, and SOFA","[813, 69, 1128, 88]",noise,0.9,"[""header label""]",noise,0.9,,unknown_like,none,False,False
13,3,text,eTable 3. Performance Metrics for Admissions With Respect to Organ Dysfunction Among 1324 EHR-Confirmed Admissions,"[90, 115, 780, 157]",body_paragraph,0.6,"[""default body_paragraph for text label""]",body_paragraph,0.6,,unknown_like,none,True,True
13,4,text,"eTable 4. Performance Metrics for Admissions With Respect to Time Zero for 1324 EHR-Confirmed Admissions, PSS Only","[90, 159, 778, 201]",body_paragraph,0.6,"[""default body_paragraph for text label""]",body_paragraph,0.6,,unknown_like,none,True,True
13,5,text,SUPPLEMENT 2.,"[91, 225, 206, 246]",unknown_structural,0.3,"[""short text, uncertain role""]",unknown_structural,0.3,,unknown_like,short_fragment,False,True
13,6,text,Data Sharing Statement,"[92, 249, 253, 269]",body_paragraph,0.6,"[""default body_paragraph for text label""]",body_paragraph,0.6,,unknown_like,none,True,True
13,7,footer,JAMA Network Open. 2023;6(8):e2329729. doi:10.1001/jamanetworkopen.2023.29729,"[92, 1468, 619, 1490]",noise,0.9,"[""footer label""]",noise,0.9,,unknown_like,none,False,False
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1 page block_id raw_label content_preview bbox role role_confidence evidence seed_role seed_confidence zone style_family marker_type render_default index_default
2 1 0 header JAMA Network | Open™ [91, 46, 346, 101] noise 0.9 ["header label"] noise 0.9 frontmatter_main_zone support_like none False False
3 1 1 header_image [1094, 52, 1127, 97] unknown_structural 0.2 ["unrecognized label 'header_image'"] unknown_structural 0.2 frontmatter_main_zone support_like empty False True
4 1 2 text Original Investigation | Health Informatics [91, 140, 414, 163] body_paragraph 0.6 ["default body_paragraph for text label"] body_paragraph 0.6 frontmatter_main_zone support_like none True True
5 1 3 doc_title Sepsis Prediction Model for Determining Sepsis vs SIRS, qSOFA, and SOFA [90, 166, 1023, 202] paper_title 0.8 ["page-1 zone title_zone: Sepsis Prediction Model for Determining Sepsis vs SIRS, qSOF"] paper_title 0.8 frontmatter_main_zone support_like none True True
6 1 4 text Adam R. Schertz, MD, MS; Kristin M. Lenoir, MPH; Alain G. Bertoni, MD, MPH; Beverly J. Levine, PhD; Morgana Mongraw-Chaffin, PhD; Karl W. Thomas, MD [90, 215, 1040, 238] unknown_structural 0.8 ["page-1 zone author_zone: Adam R. Schertz, MD, MS; Kristin M. Lenoir, MPH; Alain G. Be"] authors 0.8 body_zone reference_like citation_line False True
7 1 5 paragraph_title Abstract [91, 284, 182, 309] abstract_heading 0.95 ["abstract heading"] abstract_heading 0.95 frontmatter_main_zone heading_like short_fragment True True
8 1 6 abstract IMPORTANCE The Sepsis Prediction Model (SPM) is a proprietary decision support tool created by Epic Systems; it generates a predicting sepsis score (PSS). The model has not undergone validation agains [89, 328, 787, 460] abstract_body 0.85 ["abstract label from Paddle OCR"] abstract_body 0.85 frontmatter_main_zone support_like none True True
9 1 7 text OBJECTIVE To assess the validity and timeliness of the SPM compared with SIRS, qSOFA, and SOFA. [90, 484, 794, 510] frontmatter_noise 0.7 ["keyword-like block: OBJECTIVE To assess the validity and timeliness of the SPM c"] frontmatter_noise 0.7 frontmatter_main_zone support_like none False False
10 1 8 text DESIGN, SETTING, AND PARTICIPANTS This retrospective cohort study included all adults admitted to 5 acute care hospitals in a single US health system between June 5, 2019, and December 31, 2020. Data [90, 537, 793, 615] unknown_structural 0.8 ["page-1 zone author_zone: DESIGN, SETTING, AND PARTICIPANTS This retrospective cohort "] authors 0.8 body_zone body_like none False True
11 1 9 text MAIN OUTCOMES AND MEASURES A sepsis event was defined as receipt of 4 or more days of antimicrobials, blood cultures collected within ±48 hours of initial antimicrobial, and at least 1 organ dysfuncti [89, 640, 796, 745] body_paragraph 0.6 ["default body_paragraph for text label"] body_paragraph 0.6 body_zone body_like none True True
12 1 10 text RESULTS Of 60 507 total admissions, 1663 (2.7%) met sepsis criteria, with 1324 electronic health record-confirmed sepsis (699 [52.8%] male patients; 298 [22.5%] Black patients; 46 [3.5%] Hispanic/Lati [88, 770, 792, 1135] body_paragraph 0.6 ["default body_paragraph for text label"] body_paragraph 0.6 body_zone body_like none True True
13 1 11 text CONCLUSIONS AND RELEVANCE In this cohort study of hospital admissions, balanced accuracy of the SPM outperformed other models at higher threshold PSS; however, application of the SPM in a clinical set [89, 1159, 791, 1263] body_paragraph 0.6 ["default body_paragraph for text label"] body_paragraph 0.6 body_zone body_like none True True
14 1 12 paragraph_title Key Points [845, 288, 936, 312] paper_title 0.7 ["unnumbered paragraph_title in title zone on page 1: Key Points"] paper_title 0.7 frontmatter_main_zone support_like short_fragment True True
15 1 13 text Question Does the Sepsis Prediction Model (SPM) outperform other sepsis prediction scores with respect to validity and timeliness? [844, 318, 1117, 419] structured_insert_candidate 0.6 ["default body_paragraph for text label"] body_paragraph 0.6 frontmatter_main_zone support_like none False False
16 1 14 text Findings This cohort study of 60 507 adult admissions found that although balanced accuracy of the SPM at a predicting sepsis score (PSS) threshold of 8 or greater was better than that of the quick Se [844, 432, 1112, 743] authors 0.8 ["page-1 zone author_zone: Findings This cohort study of 60 507 adult admissions found "] authors 0.8 frontmatter_main_zone support_like none True True
17 1 15 text Meaning While the balanced accuracy of the SPM was better than qSOFA, SOFA, and SIRS at higher-threshold PSS, it had poor timeliness for sepsis prediction. [844, 754, 1114, 884] structured_insert_candidate 0.6 ["default body_paragraph for text label"] body_paragraph 0.6 body_zone body_like none False False
18 1 16 text ✗ Invited Commentary [835, 920, 1002, 945] body_paragraph 0.6 ["default body_paragraph for text label"] body_paragraph 0.6 body_zone body_like none True True
19 1 17 text Supplemental content [835, 958, 1012, 982] body_paragraph 0.6 ["default body_paragraph for text label"] body_paragraph 0.6 body_zone body_like none True True
20 1 18 text Author affiliations and article information are listed at the end of this article. [833, 995, 1098, 1036] body_paragraph 0.6 ["default body_paragraph for text label"] body_paragraph 0.6 body_zone body_like none True True
21 1 19 footnote JAMA Network Open. 2023;6(8):e2329729. doi:10.1001/jamanetworkopen.2023.29729 [89, 1289, 598, 1311] footnote 0.7 ["footnote label: JAMA Network Open. 2023;6(8):e2329729. doi:10.1001/jamanetwo"] footnote 0.7 body_zone body_like none True True
22 1 20 footer ☐ Open Access. This is an open access article distributed under the terms of the CC-BY License. [91, 1431, 648, 1454] noise 0.9 ["footer label"] noise 0.9 body_zone body_like none False False
23 1 21 footer JAMA Network Open. 2023;6(8):e2329729. doi:10.1001/jamanetworkopen.2023.29729 [91, 1469, 599, 1490] noise 0.9 ["footer label"] noise 0.9 body_zone body_like none False False
24 1 22 footer August 25, 2023 [980, 1469, 1082, 1489] noise 0.9 ["footer label"] noise 0.9 body_zone body_like short_fragment False False
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27 2 0 header JAMA Network Open | Health Informatics [92, 67, 409, 89] noise 0.9 ["header label"] noise 0.9 frontmatter_side_zone support_like none False False
28 2 1 header Sepsis Prediction Model vs SIRS, qSOFA, and SOFA [813, 68, 1128, 88] noise 0.9 ["header label"] noise 0.9 frontmatter_side_zone support_like none False False
29 2 2 paragraph_title Introduction [92, 116, 219, 139] section_heading 0.9 ["explicit scholarly heading: Introduction"] section_heading 0.9 frontmatter_side_zone heading_like canonical_section_name True True
30 2 3 text The Third International Task Force on Sepsis and Septic Shock (Sepsis-3) established a consensus definition of sepsis as "life-threatening organ dysfunction caused by a dysregulated host response to i [90, 154, 793, 489] body_paragraph 0.6 ["default body_paragraph for text label"] body_paragraph 0.6 body_zone body_like none True True
31 2 4 text Sepsis identification systems based on bedside findings are widely disseminated to promote early recognition and prompt intervention. Current recommendations emphasize that sepsis be considered a medi [90, 493, 794, 750] body_paragraph 0.6 ["default body_paragraph for text label"] body_paragraph 0.6 body_zone body_like none True True
32 2 5 text The Sepsis Prediction Model (SPM; Epic Systems) is a proprietary algorithm developed from a pooled sample of 405 000 patient encounters across 3 health care organizations between 2013 and 2015. $ ^{[3 [90, 752, 792, 1164] affiliation 0.8 ["page-1 zone affiliation_zone: The Sepsis Prediction Model (SPM; Epic Systems) is a proprie"] affiliation 0.8 frontmatter_main_zone support_like none True True
33 2 6 paragraph_title Methods Background [92, 1211, 199, 1275] subsection_heading 0.6 ["unnumbered paragraph_title, inferred level subsection_heading: Methods Background"] subsection_heading 0.6 body_zone heading_like short_fragment True True
34 2 7 footer [92, 1252, 199, 1275] noise 0.9 ["footer label"] noise 0.9 body_zone body_like empty False False
35 2 8 text This is a retrospective cohort study of all adult (>17 years) admissions within 5 hospitals of Wake Forest Baptist Health (Winston-Salem, North Carolina) beginning June 5, 2019, through December 31, 2 [90, 1279, 793, 1407] body_paragraph 0.6 ["default body_paragraph for text label"] body_paragraph 0.6 body_zone body_like none True True
36 2 9 footer JAMA Network Open. 2023;6(8):e2329729. doi:10.1001/jamanetworkopen.2023.29729 [92, 1468, 619, 1490] noise 0.9 ["footer label"] noise 0.9 unknown_like none False False
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40 3 0 header JAMA Network Open | Health Informatics [93, 67, 409, 89] noise 0.9 ["header label"] noise 0.9 body_zone body_like none False False
41 3 1 header Sepsis Prediction Model vs SIRS, qSOFA, and SOFA [813, 68, 1128, 89] noise 0.9 ["header label"] noise 0.9 body_zone body_like none False False
42 3 2 text study followed the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting guidelines. $ ^{35} $ [90, 114, 791, 164] body_paragraph 0.6 ["default body_paragraph for text label"] body_paragraph 0.6 body_zone body_like none True True
43 3 3 paragraph_title Study Population [91, 192, 241, 215] subsection_heading 0.6 ["unnumbered paragraph_title, inferred level subsection_heading: Study Population"] subsection_heading 0.6 body_zone heading_like short_fragment True True
44 3 4 text Structured data from the EHR identified inpatient admissions and all associated characteristics reported in this study. We handled potential missing data exactly how an applied algorithm would handle [90, 218, 783, 502] body_paragraph 0.6 ["default body_paragraph for text label"] body_paragraph 0.6 body_zone body_like none True True
45 3 5 paragraph_title Exclusion Criteria [91, 529, 242, 551] subsection_heading 0.6 ["unnumbered paragraph_title, inferred level subsection_heading: Exclusion Criteria"] subsection_heading 0.6 body_zone heading_like short_fragment True True
46 3 6 text For the analysis, we excluded admissions to a Burn Service, length of stay more than 30 days, those who left against medical advice, and transfers from an out-of-system facility or between internal si [90, 556, 785, 710] body_paragraph 0.6 ["default body_paragraph for text label"] body_paragraph 0.6 body_zone body_like none True True
47 3 7 paragraph_title Sepsis Definition [91, 737, 238, 760] subsection_heading 0.6 ["unnumbered paragraph_title, inferred level subsection_heading: Sepsis Definition"] subsection_heading 0.6 body_zone heading_like short_fragment True True
48 3 8 text EHR-confirmed sepsis was defined by the Centers for Disease Control and Prevention (CDC) Adult Sepsis Event (ASE) criteria of (1) 4 or more days of qualifying antimicrobial therapy beginning within $ [88, 763, 796, 1075] body_paragraph 0.6 ["default body_paragraph for text label"] body_paragraph 0.6 body_zone body_like none True True
49 3 9 paragraph_title Electronic Sepsis Alert Systems [91, 1101, 355, 1124] subsection_heading 0.6 ["unnumbered paragraph_title, inferred level subsection_heading: Electronic Sepsis Alert Systems"] subsection_heading 0.6 body_zone heading_like none True True
50 3 10 text An electronic sepsis alert based on the SPM was active during all phases of care at a single site within the health system, with the alert threshold set to a PSS of 10 or greater. All other study site [90, 1127, 790, 1255] body_paragraph 0.6 ["default body_paragraph for text label"] body_paragraph 0.6 body_zone body_like none True True
51 3 11 paragraph_title Time Zero [92, 1283, 182, 1305] subsection_heading 0.6 ["unnumbered paragraph_title, inferred level subsection_heading: Time Zero"] subsection_heading 0.6 body_zone heading_like short_fragment True True
52 3 12 text Time zero was set as 15 minutes prior to first clinician action, as defined by the initial order for antimicrobials or blood cultures. As stated previously, the SPM recalculates the PSS every 15 minut [90, 1308, 794, 1436] body_paragraph 0.6 ["default body_paragraph for text label"] body_paragraph 0.6 body_zone body_like none True True
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58 4 1 header JAMA Network Open | Health Informatics [93, 67, 409, 89] noise 0.9 ["header label"] noise 0.9 body_zone body_like none False False
59 4 2 header Sepsis Prediction Model vs SIRS, qSOFA, and SOFA [813, 69, 1128, 88] noise 0.9 ["header label"] noise 0.9 body_zone body_like none False False
60 4 3 text action related to suspicion or treatment of sepsis. For example, if a clinician assessed a patient and ordered antibiotics or a blood culture at 2:00 PM, time zero for that episode of care would be se [91, 115, 785, 188] body_paragraph 0.6 ["default body_paragraph for text label"] body_paragraph 0.6 body_zone body_like none True True
61 4 4 text The times elapsed for SIRS, qSOFA, and SOFA to reach threshold score were assessed at 1 hour. These intervals reflected the time from when any first criterion was present to the time when 2 or more cr [89, 193, 786, 319] body_paragraph 0.6 ["default body_paragraph for text label"] body_paragraph 0.6 body_zone body_like none True True
62 4 5 paragraph_title Outcomes [91, 350, 185, 370] sub_subsection_heading 0.6 ["unnumbered paragraph_title, inferred level sub_subsection_heading: Outcomes"] sub_subsection_heading 0.6 body_zone heading_like short_fragment True True
63 4 6 text Our primary goal was to compare the performance of the SPM in classifying sepsis admissions relative to SIRS, qSOFA, and SOFA, and examine the timeliness of each tool with respect to time zero. Perfor [90, 375, 793, 476] body_paragraph 0.6 ["default body_paragraph for text label"] body_paragraph 0.6 body_zone body_like none True True
64 4 7 paragraph_title Statistical Analysis [92, 504, 252, 526] subsection_heading 0.6 ["unnumbered paragraph_title, inferred level subsection_heading: Statistical Analysis"] subsection_heading 0.6 body_zone heading_like none True True
65 4 8 text We performed statistical analysis using R Statistical Computing Environment version 4.0.5 (R Core Team). We assessed differences in baseline characteristics describing qualifying admissions across 3 g [89, 530, 793, 1023] body_paragraph 0.6 ["default body_paragraph for text label"] body_paragraph 0.6 body_zone body_like none True True
66 4 9 paragraph_title Results [92, 1068, 169, 1092] section_heading 0.9 ["explicit scholarly heading: Results"] section_heading 0.9 body_zone heading_like canonical_section_name True True
67 4 10 text Of 90 773 adult admissions recorded from June 5, 2019, through December 31, 2020, our analytic data set included 60 507 admissions, comprising 49 369 unique patients, that met the inclusion criteria ( [89, 1108, 795, 1442] body_paragraph 0.6 ["default body_paragraph for text label"] body_paragraph 0.6 body_zone body_like none True True
68 4 11 footer JAMA Network Open. 2023;6(8):e2329729. doi:10.1001/jamanetworkopen.2023.29729 [92, 1468, 619, 1490] noise 0.9 ["footer label"] noise 0.9 unknown_like none False False
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73 5 1 header JAMA Network Open | Health Informatics [92, 68, 409, 88] noise 0.9 ["header label"] noise 0.9 body_zone body_like none False False
74 5 2 header Sepsis Prediction Model vs SIRS, qSOFA, and SOFA [813, 69, 1128, 88] noise 0.9 ["header label"] noise 0.9 body_zone body_like none False False
75 5 3 text of which were diagnosed with COVID-19. Those with EHR-confirmed sepsis were older and more likely to be classified as White compared with all-cause admissions. Immunocompromised status, atheroscleroti [89, 115, 793, 372] body_paragraph 0.6 ["default body_paragraph for text label"] body_paragraph 0.6 body_zone body_like none True True
76 5 4 text We observed an inverse association between an increase in PSS threshold and sensitivity and a positive association for higher PSS threshold and specificity (Table 2). For the entire length of admissio [89, 376, 794, 630] body_paragraph 0.6 ["default body_paragraph for text label"] body_paragraph 0.6 body_zone body_like none True True
77 5 5 text All the prediction scores had a median time to threshold after time zero (Table 3). SIRS criteria were positive in the largest proportion of EHR-confirmed sepsis admissions before time zero (573 [43.3 [89, 634, 795, 841] body_paragraph 0.6 ["default body_paragraph for text label"] body_paragraph 0.6 body_zone body_like none True True
78 5 6 image [92, 894, 1085, 1400] media_asset 0.85 ["media label: image"] media_asset 0.85 body_zone body_like empty True True
79 5 7 vision_footnote ASE indicates Adult Sepsis Event; BP, blood pressure; LOS, length of stay. [91, 1423, 512, 1443] footnote 0.7 ["vision_footnote label: ASE indicates Adult Sepsis Event; BP, blood pressure; LOS, l"] footnote 0.7 body_zone body_like none True True
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84 6 0 header JAMA Network Open | Health Informatics [92, 66, 409, 89] noise 0.9 ["header label"] noise 0.9 body_zone body_like none False False
85 6 1 header Sepsis Prediction Model vs SIRS, qSOFA, and SOFA [813, 68, 1128, 88] noise 0.9 ["header label"] noise 0.9 body_zone body_like none False False
86 6 2 text of onset of organ dysfunction by eSOFA criteria, we saw similar trends, with SIRS performing best, followed by SOFA, qSOFA, and the SPM (eTable 3 in Supplement 1). Results pertaining to additional PSS [90, 114, 785, 191] body_paragraph 0.6 ["default body_paragraph for text label"] body_paragraph 0.6 body_zone body_like none True True
87 6 3 paragraph_title Discussion [92, 237, 200, 263] section_heading 0.9 ["explicit scholarly heading: Discussion"] section_heading 0.9 body_zone heading_like canonical_section_name True True
88 6 4 text Although the SPM demonstrated better balanced accuracy and specificity for sepsis at higher-threshold PSS (8 to 10), it also missed a higher proportion of true cases and was far less timely in compari [90, 277, 794, 407] body_paragraph 0.6 ["default body_paragraph for text label"] body_paragraph 0.6 body_zone body_like none True True
89 6 5 figure_title Table 1. Baseline Characteristics of Inpatient Admissions by Sepsis Category $ ^{a} $ [93, 457, 574, 479] table_caption 0.9 ["table prefix matched: Table 1. Baseline Characteristics of Inpatient Admissions by"] table_caption 0.9 display_zone table_caption_like table_number True True
90 6 6 table <table><tr><td rowspan="2">Characteristic</td><td colspan="3">Admissions, No. (%)</td></tr><tr><td>ASE sepsis (n = 1324)</td><td>COVID-19 sepsis (n = 339)</td><td>Nonsepsis (n = 58 844)</td></tr><tr>< [94, 466, 792, 1438] media_asset 0.85 ["media label: table"] media_asset 0.85 body_zone body_like none True True
91 6 7 text Abbreviations: ASE, Adult Sepsis Event; ED, emergency department; ICU, intensive care unit. [810, 1118, 1095, 1159] body_paragraph 0.6 ["default body_paragraph for text label"] body_paragraph 0.6 body_zone body_like none True True
92 6 8 text $ ^{a} $ $ \chi^{2} $ test was conducted for categorical variables. Kruskal-Wallis test was implemented for continuous, skewed variables. [810, 1163, 1126, 1223] body_paragraph 0.6 ["default body_paragraph for text label"] body_paragraph 0.6 body_zone body_like affiliation_marker True True
93 6 9 text $ ^{b} $ Other is inclusive of categories in the electronic health record labeled as American Indian or Alaska Native, Asian, multiracial, Native Hawaiian or Other Pacific Islander, and Other (not fu [810, 1228, 1120, 1308] body_paragraph 0.6 ["default body_paragraph for text label"] body_paragraph 0.6 body_zone body_like affiliation_marker True True
94 6 10 text Body mass index is calculated as weight in kilograms divided by height in meters squared. [810, 1313, 1125, 1353] body_paragraph 0.6 ["default body_paragraph for text label"] body_paragraph 0.6 body_zone body_like none True True
95 6 11 vision_footnote JAMA Network Open. 2023;6(8):e2329729. doi:10.1001/jamanetworkopen.2023.29729 [92, 1468, 619, 1490] footnote 0.7 ["vision_footnote label: JAMA Network Open. 2023;6(8):e2329729. doi:10.1001/jamanetwo"] footnote 0.7 unknown_like none True True
96 6 12 text $ ^{d} $ The P value for body mass of 30 or greater was .005, and P values for all other variables were <.001. [810, 1357, 1127, 1398] body_paragraph 0.6 ["default body_paragraph for text label"] body_paragraph 0.6 body_zone body_like affiliation_marker True True
97 6 13 text $ ^{e} $ Immunocompromised indicates patient with HIV/AIDS or metastatic cancer. [810, 1403, 1083, 1444] body_paragraph 0.6 ["default body_paragraph for text label"] body_paragraph 0.6 body_zone body_like affiliation_marker True True
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102 7 1 header JAMA Network Open | Health Informatics [93, 67, 409, 89] noise 0.9 ["header label"] noise 0.9 body_zone body_like none False False
103 7 2 header Sepsis Prediction Model vs SIRS, qSOFA, and SOFA [813, 69, 1128, 89] noise 0.9 ["header label"] noise 0.9 body_zone body_like none False False
104 7 3 text with increased score complexity and lack of transparency of the SPM epitomizes its major flaw: it appears to predict sepsis long after the clinician has recognized possible sepsis and acted on that su [90, 114, 793, 214] body_paragraph 0.6 ["default body_paragraph for text label"] body_paragraph 0.6 body_zone body_like none True True
105 7 4 text At higher PSS scores (8 to 10), only 12.9% to 19.7% of patients could have been identified by the SPM in a clinically relevant time prior to clinician action (Table 3). In addition to poor timeliness [90, 218, 792, 398] body_paragraph 0.6 ["default body_paragraph for text label"] body_paragraph 0.6 body_zone body_like none True True
106 7 5 text Given the existing observational data regarding the importance of early antimicrobial administration, the patients who would benefit most from early recognition, resource allocation, and appropriate a [90, 401, 796, 580] body_paragraph 0.6 ["default body_paragraph for text label"] body_paragraph 0.6 body_zone body_like none True True
107 7 6 figure_title Table 2. Performance Metrics for Classification of Admission (All Sepsis vs Nonsepsis) [92, 615, 630, 635] table_caption 0.9 ["table prefix matched: Table 2. Performance Metrics for Classification of Admission"] table_caption 0.9 display_zone table_caption_like table_number True True
108 7 7 table <table><tr><td rowspan="2">All sepsis classification (n = 1663)</td><td colspan="7">Performance metric (95% CI)</td></tr><tr><td>Accuracy</td><td>Sensitivity</td><td>Specificity</td><td>False negative [94, 641, 1124, 911] media_asset 0.85 ["media label: table"] media_asset 0.85 body_zone body_like none True True
109 7 8 vision_footnote Abbreviations: PSS, Predicting Sepsis Score; qSOFA, quick Sepsis-Related Organ Failure Assessment; SIRS, Systemic Inflammatory Response Syndrome; SOFA, Sequential Organ Failure Assessment. [92, 919, 600, 978] footnote 0.7 ["vision_footnote label: Abbreviations: PSS, Predicting Sepsis Score; qSOFA, quick Se"] footnote 0.7 body_zone body_like none True True
110 7 9 vision_footnote $ ^{c} $ Balanced accuracy is calculated as sensitivity plus specificity divided by 2. [618, 919, 1051, 938] footnote 0.7 ["vision_footnote label: $ ^{c} $ Balanced accuracy is calculated as sensitivity plus"] footnote 0.7 body_zone body_like affiliation_marker True True
111 7 10 vision_footnote $ ^{a} $ False negative indicates the proportion of missed true sepsis cases. [92, 984, 490, 1003] footnote 0.7 ["vision_footnote label: $ ^{a} $ False negative indicates the proportion of missed t"] footnote 0.7 body_zone body_like affiliation_marker True True
112 7 11 vision_footnote $ ^{d} $ Diagnostic odds ratio is calculated as the positive likelihood ratio divided by the negative likelihood ratio. [618, 944, 1084, 984] footnote 0.7 ["vision_footnote label: $ ^{d} $ Diagnostic odds ratio is calculated as the positive"] footnote 0.7 body_zone body_like affiliation_marker True True
113 7 12 vision_footnote $ ^{b} $ False positive indicates the proportion of nonsepsis cases falsely classified as sepsis. [92, 1009, 584, 1029] footnote 0.7 ["vision_footnote label: $ ^{b} $ False positive indicates the proportion of nonsepsi"] footnote 0.7 body_zone body_like affiliation_marker True True
114 7 13 figure_title Table 3. Performance Metrics for Admissions With Respect to Time Zero Among 1324 Electronic Health Record-Confirmed Sepsis Admissions $ ^{a} $ [92, 1075, 740, 1115] table_caption 0.9 ["table prefix matched: Table 3. Performance Metrics for Admissions With Respect to "] table_caption 0.9 display_zone table_caption_like table_number True True
115 7 14 table <table><tr><td rowspan="2">Performance metric</td><td colspan="4">Diagnostic criteria, No. (%)</td></tr><tr><td>PSS</td><td>SIRS</td><td>qSOFA</td><td>SOFA</td></tr><tr><td>Difference between time of [94, 1119, 790, 1279] media_asset 0.85 ["media label: table"] media_asset 0.85 body_zone body_like none True True
116 7 15 vision_footnote Abbreviations: PSS, Predicting Sepsis Score; qSOFA, quick Sepsis-Related Organ Failure Assessment; SIRS, Systemic Inflammatory Response Syndrome; SOFA, Sequential Organ Failure Assessment. [91, 1287, 755, 1326] footnote 0.7 ["vision_footnote label: Abbreviations: PSS, Predicting Sepsis Score; qSOFA, quick Se"] footnote 0.7 body_zone body_like none True True
117 7 16 vision_footnote $ ^{a} $ Sepsis is defined as meeting Adult Sepsis Event criteria. [92, 1333, 423, 1353] footnote 0.7 ["vision_footnote label: $ ^{a} $ Sepsis is defined as meeting Adult Sepsis Event cri"] footnote 0.7 body_zone body_like affiliation_marker True True
118 7 17 vision_footnote Time zero indicates 15 minutes before clinician action (blood culture or antimicrobial order). Positive and negative values indicate threshold being met before and after time zero, respectively. Diffe [91, 1357, 792, 1416] footnote 0.7 ["vision_footnote label: Time zero indicates 15 minutes before clinician action (bloo"] footnote 0.7 body_zone body_like none True True
119 7 18 vision_footnote Threshold score for PSS was 8 or greater; SIRS, 2 or greater; qSOFA, 2 or greater; SOFA, 2 or greater. [92, 1423, 674, 1443] footnote 0.7 ["vision_footnote label: Threshold score for PSS was 8 or greater; SIRS, 2 or greater"] footnote 0.7 body_zone body_like none True True
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124 8 0 header JAMA Network Open | Health Informatics [92, 65, 409, 89] noise 0.9 ["header label"] noise 0.9 body_zone body_like none False False
125 8 1 header Sepsis Prediction Model vs SIRS, qSOFA, and SOFA [813, 67, 1128, 89] noise 0.9 ["header label"] noise 0.9 body_zone body_like none False False
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127 8 3 vision_footnote JAMA Network Open. 2023;6(8):e2329729. doi:10.1001/jamanetworkopen.2023.29729 [92, 1468, 619, 1490] footnote 0.7 ["vision_footnote label: JAMA Network Open. 2023;6(8):e2329729. doi:10.1001/jamanetwo"] footnote 0.7 unknown_like none True True
128 8 4 vision_footnote This figure demonstrates the difference between time zero and time of sepsis threshold positivity. They are negative and are shaded in blue. Those admissions with a threshold score after time 15 minut [984, 136, 1062, 1442] footnote 0.7 ["vision_footnote label: This figure demonstrates the difference between time zero an"] footnote 0.7 body_zone body_like none True True
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131 8 7 number 8/13 [1099, 1470, 1127, 1489] noise 0.9 ["page number label"] noise 0.9 body_zone body_like short_fragment False False
132 9 0 header JAMA Network Open | Health Informatics [93, 67, 410, 89] noise 0.9 ["header label"] noise 0.9 body_zone body_like none False False
133 9 1 header Sepsis Prediction Model vs SIRS, qSOFA, and SOFA [813, 69, 1128, 89] noise 0.9 ["header label"] noise 0.9 body_zone body_like none False False
134 9 2 text existing limitations to timeliness that may be due to other aspects of model development in order to meaningfully impact sepsis recognition or management. [90, 115, 790, 164] body_paragraph 0.6 ["default body_paragraph for text label"] body_paragraph 0.6 body_zone body_like none True True
135 9 3 paragraph_title Strengths and Limitations [91, 193, 311, 215] subsection_heading 0.6 ["unnumbered paragraph_title, inferred level subsection_heading: Strengths and Limitations"] subsection_heading 0.6 body_zone heading_like none True True
136 9 4 text The strengths of our study include a large sample size and inclusion of patients from both community-based and tertiary referral hospitals. Utilization of the CDC ASE criteria as well as separately de [89, 219, 795, 501] body_paragraph 0.6 ["default body_paragraph for text label"] body_paragraph 0.6 body_zone body_like none True True
137 9 5 text This study also has limitations, including investigation of only a single health system, the observational study design, the inherent heterogeneity of sepsis, reliance on EHR data, and missing data. B [89, 504, 795, 763] body_paragraph 0.6 ["default body_paragraph for text label"] body_paragraph 0.6 body_zone body_like none True True
138 9 6 paragraph_title Conclusions [92, 808, 213, 833] section_heading 0.9 ["explicit scholarly heading: Conclusions"] section_heading 0.9 body_zone heading_like canonical_section_name True True
139 9 7 text In this cohort study of 60 507 hospital admissions, we found that although the SPM marginally outperformed existing prediction scores in balanced accuracy for classification of sepsis, it suffers from [90, 849, 796, 1107] body_paragraph 0.6 ["default body_paragraph for text label"] body_paragraph 0.6 body_zone body_like none True True
140 9 8 text ARTICLE INFORMATION [91, 1176, 256, 1196] unknown_structural 0.3 ["short text, uncertain role"] unknown_structural 0.3 body_zone body_like short_fragment False True
141 9 9 text Accepted for Publication: July 7, 2023. [91, 1198, 342, 1220] body_paragraph 0.6 ["default body_paragraph for text label"] body_paragraph 0.6 body_zone body_like none True True
142 9 10 footnote Published: August 25, 2023. doi:10.1001/jamanetworkopen.2023.29729 [90, 1226, 545, 1248] footnote 0.7 ["footnote label: Published: August 25, 2023. doi:10.1001/jamanetworkopen.2023"] footnote 0.7 unknown_like none True True
143 9 11 footnote Open Access: This is an open access article distributed under the terms of the CC-BY License. © 2023 Schertz AR et al. JAMA Network Open. [90, 1254, 788, 1297] footnote 0.7 ["footnote label: Open Access: This is an open access article distributed unde"] footnote 0.7 body_zone body_like none True True
144 9 12 footnote Corresponding Author: Adam R. Schertz, MD, MS, Section of Pulmonology, Critical Care, Allergy & Immunologic Diseases, Atrium Health Wake Forest Baptist, One Medical Center Boulevard, Winston-Salem, NC [90, 1303, 819, 1370] footnote 0.7 ["footnote label: Corresponding Author: Adam R. Schertz, MD, MS, Section of Pu"] footnote 0.7 body_zone body_like none True True
145 9 13 footnote Author Affiliations: Department of Internal Medicine, Atrium Health Wake Forest Baptist, Winston-Salem, North Carolina (Schertz, Bertoni, Thomas); Section of Pulmonology, Critical Care, Allergy and Im [91, 1376, 797, 1443] footnote 0.7 ["footnote label: Author Affiliations: Department of Internal Medicine, Atrium"] footnote 0.7 body_zone body_like none True True
146 9 14 footer JAMA Network Open. 2023;6(8):e2329729. doi:10.1001/jamanetworkopen.2023.29729 [92, 1468, 619, 1490] noise 0.9 ["footer label"] noise 0.9 unknown_like none False False
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150 10 0 header JAMA Network Open | Health Informatics [93, 67, 409, 88] noise 0.9 ["header label"] noise 0.9 body_zone body_like none False False
151 10 1 header Sepsis Prediction Model vs SIRS, qSOFA, and SOFA [813, 69, 1128, 88] noise 0.9 ["header label"] noise 0.9 body_zone body_like none False False
152 10 2 text and Data Science, Division of Public Health Science, Atrium Health Wake Forest Baptist, Winston-Salem, North Carolina (Lenoir, Bertoni); Social Sciences and Health Policy, Wake Forest University Schoo [91, 116, 786, 202] body_paragraph 0.6 ["default body_paragraph for text label"] body_paragraph 0.6 body_zone body_like none True True
153 10 3 text Author Contributions: Dr Schertz and Ms Lenoir had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis. [90, 210, 720, 251] frontmatter_noise 0.88 ["default body_paragraph for text label", "late role resolution: editorial phrase cross-validates non-body classification", "zone=body_zone", "style_family=support_like"] body_paragraph 0.6 body_zone support_like none False False
154 10 4 text Concept and design: Schertz, Bertoni, Levine, Mongraw-Chaffin, Thomas. [91, 259, 544, 279] body_paragraph 0.6 ["default body_paragraph for text label"] body_paragraph 0.6 body_zone body_like none True True
155 10 5 text Acquisition, analysis, or interpretation of data: Schertz, Lenoir, Bertoni, Levine, Mongraw-Chaffin. [91, 287, 687, 306] body_paragraph 0.6 ["default body_paragraph for text label"] body_paragraph 0.6 body_zone body_like none True True
156 10 6 text Drafting of the manuscript: Schertz, Lenoir, Thomas. [92, 315, 416, 335] body_paragraph 0.6 ["default body_paragraph for text label"] body_paragraph 0.6 body_zone body_like none True True
157 10 7 text Critical review of the manuscript for important intellectual content: Schertz, Lenoir, Bertoni, Levine, Mongraw-Chaffin. [91, 344, 764, 383] body_paragraph 0.6 ["default body_paragraph for text label"] body_paragraph 0.6 body_zone body_like none True True
158 10 8 text Statistical analysis: Lenoir, Levine, Mongraw-Chaffin. [92, 393, 419, 412] body_paragraph 0.6 ["default body_paragraph for text label"] body_paragraph 0.6 body_zone body_like none True True
159 10 9 text Obtained funding: Schertz. [92, 421, 261, 440] body_paragraph 0.6 ["default body_paragraph for text label"] body_paragraph 0.6 body_zone body_like none True True
160 10 10 text Administrative, technical, or material support: Bertoni. [91, 449, 427, 468] body_paragraph 0.6 ["default body_paragraph for text label"] body_paragraph 0.6 body_zone body_like none True True
161 10 11 text Supervision: Schertz, Thomas. [92, 477, 281, 495] body_paragraph 0.6 ["default body_paragraph for text label"] body_paragraph 0.6 body_zone body_like none True True
162 10 12 text Conflict of Interest Disclosures: None reported. [92, 505, 397, 523] frontmatter_noise 0.6 ["default body_paragraph for text label", "frontmatter_side_zone excluded from body flow"] frontmatter_noise 0.6 frontmatter_side_zone support_like none False False
163 10 13 text Funding/Support: This project was funded by the Wake Forest Clinical and Translational Science Institute. [91, 533, 743, 552] body_paragraph 0.6 ["default body_paragraph for text label"] body_paragraph 0.6 body_zone body_like none True True
164 10 14 text Role of the Funder/Sponsor: The funding source approved the design and conduct of the study, but had no role in the collection, management, analysis, and interpretation of the data; preparation, revie [92, 561, 783, 624] body_paragraph 0.6 ["default body_paragraph for text label"] body_paragraph 0.6 body_zone body_like none True True
165 10 15 paragraph_title Data Sharing Statement: See Supplement 2. [92, 633, 372, 652] subsection_heading 0.6 ["unnumbered paragraph_title, inferred level subsection_heading: Data Sharing Statement: See Supplement 2."] subsection_heading 0.6 body_zone body_like none True True
166 10 16 text Additional Contributions: We would like to thank the following people for their contributions to this work. Brian Wells, MD, PhD (Department of Family Medicine, Atrium Health Wake Forest Baptist), pro [92, 661, 794, 857] body_paragraph 0.6 ["default body_paragraph for text label"] body_paragraph 0.6 body_zone body_like none True True
167 10 17 paragraph_title REFERENCES [93, 875, 186, 893] reference_heading 0.9 ["references heading: REFERENCES"] reference_heading 0.9 reference_zone unknown_like short_fragment True True
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222 12 16 text 43. Baghdadi JD, Wong MD, Uslan DZ, et al. Adherence to the SEP-1 sepsis bundle in hospital-onset v. community-onset sepsis: a multicenter retrospective cohort study. J Gen Intern Med. 2020;35(4):1153 [92, 1012, 791, 1074] reference_item 0.6 ["reference-like pattern: 43. Baghdadi JD, Wong MD, Uslan DZ, et al. Adherence to the "] reference_item 0.6 reference_zone reference_like reference_numeric_dot True True
223 12 17 text 44. Page B, Klompas M, Chan C, et al. Surveillance for healthcare-associated infections: hospital-onset adult sepsis events versus current reportable conditions. Clin Infect Dis. 2021;73(6):1013-1019. [92, 1084, 781, 1125] reference_item 0.6 ["reference-like pattern: 44. Page B, Klompas M, Chan C, et al. Surveillance for healt"] reference_item 0.6 reference_zone reference_like reference_numeric_dot True True
224 12 18 text 45. Rhee C, Klompas M. Should hospital-onset adult sepsis event surveillance be routine... or even mandatory? Antimicrob Steward Healthc Epidemiol. 2022;2(1):e32. doi:10.1017/ash.2022.16 [92, 1133, 779, 1175] reference_item 0.6 ["reference-like pattern: 45. Rhee C, Klompas M. Should hospital-onset adult sepsis ev"] reference_item 0.6 reference_zone reference_like reference_numeric_dot True True
225 12 19 text 46. Cárdenas-Turanzas M, Ensor J, Wakefield C, et al. Cross-validation of a Sequential Organ Failure Assessment score-based model to predict mortality in patients with cancer admitted to the intensive [92, 1183, 778, 1246] reference_item 0.6 ["reference-like pattern: 46. C\u00e1rdenas-Turanzas M, Ensor J, Wakefield C, et al. Cross-"] reference_item 0.6 reference_zone reference_like reference_numeric_dot True True
226 12 20 paragraph_title SUPPLEMENT 1. [92, 1272, 202, 1290] unknown_structural 0.6 ["unnumbered paragraph_title, inferred level section_heading: SUPPLEMENT 1."] section_heading 0.6 tail_nonref_hold_zone unknown_like short_fragment False True
227 12 21 text eFigure 1. Diagnostic Odds Ratios for Sepsis, by Model [92, 1294, 431, 1314] body_paragraph 0.6 ["default body_paragraph for text label"] body_paragraph 0.6 tail_nonref_hold_zone unknown_like none True True
228 12 22 text eFigure 2. Kaplan-Meier Analysis of Time to Threshold Positivity vs Time Zero [93, 1317, 573, 1335] body_paragraph 0.6 ["default body_paragraph for text label"] body_paragraph 0.6 tail_nonref_hold_zone unknown_like none True True
229 12 23 text eFigure 3. Plot of Criteria Time With Respect to Time Zero for Each EHR-Confirmed Admission, PSS Only [92, 1339, 732, 1358] body_paragraph 0.6 ["default body_paragraph for text label"] body_paragraph 0.6 tail_nonref_hold_zone unknown_like none True True
230 12 24 text eTable 1. Missing Data Stratified by Admission Type [93, 1361, 414, 1380] body_paragraph 0.6 ["default body_paragraph for text label"] body_paragraph 0.6 tail_nonref_hold_zone unknown_like none True True
231 12 25 text eTable 2. Additional Performance Metrics for Classification of Admission [92, 1383, 540, 1402] body_paragraph 0.6 ["default body_paragraph for text label"] body_paragraph 0.6 tail_nonref_hold_zone unknown_like none True True
232 12 26 footer JAMA Network Open. 2023;6(8):e2329729. doi:10.1001/jamanetworkopen.2023.29729 [92, 1469, 618, 1489] noise 0.9 ["footer label"] noise 0.9 unknown_like none False False
233 12 27 footer August 25, 2023 [974, 1470, 1075, 1489] noise 0.9 ["footer label"] noise 0.9 unknown_like short_fragment False False
234 12 28 footer Downloaded from jamanetwork.com by guest on 05/18/2026 [2, 1550, 527, 1569] noise 0.9 ["footer label"] noise 0.9 unknown_like none False False
235 12 29 number 12/13 [1096, 1470, 1128, 1489] noise 0.9 ["page number label"] noise 0.9 unknown_like short_fragment False False
236 13 0 header_image [95, 0, 252, 19] unknown_structural 0.2 ["unrecognized label 'header_image'"] unknown_structural 0.2 unknown_like empty False True
237 13 1 header JAMA Network Open | Health Informatics [92, 67, 409, 89] noise 0.9 ["header label"] noise 0.9 unknown_like none False False
238 13 2 header Sepsis Prediction Model vs SIRS, qSOFA, and SOFA [813, 69, 1128, 88] noise 0.9 ["header label"] noise 0.9 unknown_like none False False
239 13 3 text eTable 3. Performance Metrics for Admissions With Respect to Organ Dysfunction Among 1324 EHR-Confirmed Admissions [90, 115, 780, 157] body_paragraph 0.6 ["default body_paragraph for text label"] body_paragraph 0.6 unknown_like none True True
240 13 4 text eTable 4. Performance Metrics for Admissions With Respect to Time Zero for 1324 EHR-Confirmed Admissions, PSS Only [90, 159, 778, 201] body_paragraph 0.6 ["default body_paragraph for text label"] body_paragraph 0.6 unknown_like none True True
241 13 5 text SUPPLEMENT 2. [91, 225, 206, 246] unknown_structural 0.3 ["short text, uncertain role"] unknown_structural 0.3 unknown_like short_fragment False True
242 13 6 text Data Sharing Statement [92, 249, 253, 269] body_paragraph 0.6 ["default body_paragraph for text label"] body_paragraph 0.6 unknown_like none True True
243 13 7 footer JAMA Network Open. 2023;6(8):e2329729. doi:10.1001/jamanetworkopen.2023.29729 [92, 1468, 619, 1490] noise 0.9 ["footer label"] noise 0.9 unknown_like none False False
244 13 8 footer August 25, 2023 [974, 1470, 1075, 1489] noise 0.9 ["footer label"] noise 0.9 unknown_like short_fragment False False
245 13 9 footer Downloaded from jamanetwork.com by guest on 05/18/2026 [2, 1549, 528, 1570] noise 0.9 ["footer label"] noise 0.9 unknown_like none False False
246 13 10 number 13/13 [1095, 1470, 1128, 1489] noise 0.9 ["page number label"] noise 0.9 unknown_like short_fragment False False

2
tests/test_ocr_document.py
View file

@ -4802,6 +4802,8 @@ def test_same_page_reference_boundary_is_resolved_upstream_not_in_renderer() ->
by_id = {b["block_id"]: b for b in normalized}
assert by_id["body_1"]["role"] == "body_paragraph"
assert by_id["ref_1"]["role"] == "reference_item"
assert by_id["body_1"].get("zone") != "reference_zone"
assert by_id["ref_1"].get("zone") == "reference_zone"
def test_page_text_coverage_flags_low_ratio_when_pdf_text_dominates() -> None:

26
tests/test_ocr_figures.py
View file

@ -2246,3 +2246,29 @@ def test_partition_assets_by_caption_bands_keeps_assets_local_to_caption_band()
assert [a["block_id"] for a in parts["101"]] == [1, 2]
assert [a["block_id"] for a in parts["102"]] == [3]
def test_sequential_fallback_can_match_previous_page_asset_for_next_page_caption() -> None:
from paperforge.worker.ocr_figures import build_figure_inventory
blocks = [
{"block_id": "a1", "page": 39, "role": "media_asset", "raw_label": "image", "bbox": [220, 1153, 556, 1443]},
{
"block_id": "cap3",
"page": 40,
"role": "figure_caption",
"text": "Fig. 3. Magnetic actuation characterization of MR-SCS.",
"bbox": [73, 99, 1032, 548],
"zone": "post_reference_backmatter_zone",
"style_family": "legend_like",
"marker_signature": {"type": "figure_number", "number": 3},
},
]
inventory = build_figure_inventory(blocks)
matched = [m for m in inventory["matched_figures"] if m.get("figure_number") == 3]
ambiguous = [a for a in inventory["ambiguous_figures"] if a.get("figure_number") == 3]
assert len(matched) == 1
assert matched[0]["matched_assets"][0]["block_id"] == "a1"
assert ambiguous == []

81
tests/test_ocr_health.py
View file

@ -613,3 +613,84 @@ def test_ocr_health_role_gate_degraded_forces_overall_red() -> None:
)
assert health["overall"] == "red"
def test_ocr_health_exposes_completeness_signals_for_runtime_blocks() -> None:
from paperforge.worker.ocr_health import build_ocr_health
report = build_ocr_health(
page_count=1,
raw_blocks_count=2,
structured_blocks=[
{
"role": "body_paragraph",
"text": "short text",
"page": 1,
"pdf_text": "short text plus a much longer native PDF segment that should dominate coverage",
"pdf_region_text": "short text plus a much longer native PDF segment that should dominate coverage",
},
{"role": "section_heading", "text": "Intro"},
{"role": "section_heading", "text": "Methods"},
{"role": "reference_item", "text": "[1] Ref"},
{"role": "abstract_body", "text": "Abstract."},
],
figure_inventory={},
table_inventory={},
)
assert report["page_text_coverage"][1]["page_text_coverage_status"] == "low"
assert report["text_completeness_summary"]["short_vs_pdf"] == 1
def test_ocr_health_marks_missing_pdf_baseline_without_perfect_ratio() -> None:
from paperforge.worker.ocr_health import build_ocr_health
report = build_ocr_health(
page_count=1,
raw_blocks_count=1,
structured_blocks=[
{"role": "body_paragraph", "text": "Body text", "page": 1, "pdf_text": ""},
{"role": "section_heading", "text": "Intro"},
{"role": "section_heading", "text": "Methods"},
{"role": "reference_item", "text": "[1] Ref"},
{"role": "abstract_body", "text": "Abstract."},
],
figure_inventory={},
table_inventory={},
)
coverage = report["page_text_coverage"][1]
assert coverage["page_text_coverage_status"] == "missing_pdf_text"
assert coverage["page_text_coverage_ratio_chars"] is None
def test_rendered_text_coverage_normalizes_whitespace_and_case() -> None:
from paperforge.worker.ocr_health import audit_rendered_text_coverage
result = audit_rendered_text_coverage(
rendered_markdown="In Vivo methods are summarized here.",
pdf_segments=["in vivo methods are summarized here."],
)
assert result["rendered_text_gap_count"] == 0
def test_ocr_health_reports_rendered_gap_count_when_markdown_is_provided() -> None:
from paperforge.worker.ocr_health import build_ocr_health
report = build_ocr_health(
page_count=1,
raw_blocks_count=1,
structured_blocks=[
{"role": "body_paragraph", "text": "Short OCR body.", "page": 1, "pdf_region_text": "A long methods segment that is missing from render."},
{"role": "section_heading", "text": "Intro"},
{"role": "section_heading", "text": "Methods"},
{"role": "reference_item", "text": "[1] Ref"},
{"role": "abstract_body", "text": "Abstract."},
],
figure_inventory={},
table_inventory={},
rendered_markdown="Only the introduction survived.",
)
assert report["rendered_text_gap_count"] == 1

1
tests/test_ocr_pdf_text_fallback.py
View file

@ -2,7 +2,6 @@
from pathlib import Path
import fitz
import pytest
from paperforge.worker.ocr_pdf_spans import (
_words_to_text,

8
tests/test_ocr_real_paper_audit_contracts.py
View file

@ -33,3 +33,11 @@ def test_layout_class_manifest_has_named_representatives() -> None:
assert by_tag["same_page_ref_body_split"]
assert by_tag["post_reference_biography"]
assert by_tag["review_callout"] or by_tag["special_structure"]
def test_every_audit_paper_has_at_least_one_layout_tag() -> None:
manifest = _load_manifest()
missing = [paper["paper_key"] for paper in manifest["papers"] if not paper.get("layout_tags")]
assert missing == []

4
tests/test_ocr_real_paper_regressions.py
View file

@ -474,10 +474,6 @@ def test_dwqqk2yb_ownership_no_longer_mega_merges_same_page_assets(tmp_path: Pat
assert 3 in matched or 3 in ambiguous, "Fig 3 should be captured"
@pytest.mark.xfail(
strict=True,
reason="Gate 2 remaining work: DW Fig 3 caption scores ambiguous on its mixed-layout page; needs group-first scoring refinement",
)
def test_dwqqk2yb_figure3_is_fully_owned_not_merely_captured(tmp_path: Path) -> None:
"""Gate 2 regression: DW Figure 3 must be strictly matched, not left ambiguous."""
result = replay_production_pipeline("DWQQK2YB", tmp_path)