Simple optical evaluation criteria reliably identify the post-endoscopic mucosal resection scar for benign large non-pedunculated colorectal polyps without tattoo placement
Bibliographic record
Abstract
Abstract Background Recognition of the post-endoscopic mucosal resection (EMR) scar is critical for large (≥ 20 mm) non-pedunculated colorectal polyp (LNPCP) management. The utility of intraluminal tattooing to facilitate scar identification is unknown. Methods We evaluated the ability of simple easy-to-use optical evaluation criteria to detect the post-EMR scar, with or without tattoo placement, in a prospective observational cohort of LNPCPs referred for endoscopic resection. The primary outcome was scar identification, further stratified by lesion size (20–39 mm, ≥ 40 mm) and histopathology (adenomatous, serrated). Results 1023 LNPCPs underwent both successful EMR and first surveillance colonoscopy (median size 35 mm, IQR 30–50 mm); 124 (12.1 %) had an existing tattoo or a tattoo placed at the index EMR. The post-EMR scar was identified in 1020 patients (99.7 %). The presence of a tattoo did not affect scar identification (100.0 % vs. 99.7 %; P > 0.99). There was no difference for LNPCPs 20–39 mm, LNPCPs ≥ 40 mm, adenomatous LNPCPs, and serrated LNPCPs (all P > 0.99). Conclusions The post-EMR scar can be reliably identified with simple easy-to-use optical evaluation criteria, without the need for universal tattoo placement.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".