Local Recurrence Rates After Resection of Large Colorectal Serrated Lesions
Bibliographic record
Abstract
Aims Our aim was to evaluate local recurrence rates (LRR) after resection of large colorectal serrated lesions (SLs) and the use of margin ablation in preventing recurrence. Methods Patients with resection of colorectal SSL, TSA, or HP polyps ≥ 15 mm from 2010-2022 with a colonoscopy follow-up within 18 months were identified through pathology database and electronic medical records search. Hereditary CRC syndromes, follow-ups longer than 18 months or no follow-up, surgical resection were excluded. The primary outcome was LRRs (either histologic or visual) during the first 18-month follow-up. Secondary outcomes were LRRs according to size, LRR after margin ablation and cold snare polypectomy (CSP). Results 188 polyps in 168 patients were resected (55.1% women; mean age, 63.8 years). The mean size of polyps was 22.9 mm, with 114 (60.6%) ≥20mm. 128 (67.0%) polyps were resected with hot EMR, 27 (14.4%) with CSP including 24 with submucosal injection, and 38 (20.2%) polyps received margin ablation. Mean first surveillance colonoscopy was 8.2 months. Overall LRR for the first 18-months was 13.3% (25/188) [95% confidence interval (CI) 8.8-19.0] (12.2% for polyps 15-19mm, 14.0% for ≥20mm, 14.6% for ≥30 mm). LRR was significantly lower after hot EMR with margin ablation when compared with no margin ablation (2.6% vs 17.0%; p=0.026) or CSP (2.6% vs 18.5%; p=0.029). There was no difference in LRR between EMR without margin ablation and CSP (p=0.86). Conclusions The local recurrence rate for SLs ≥15 mm is high with 13.3% overall recurrence. EMR with thermal ablation of the margins is superior to both no ablation and CSP in reducing LRRs. Publication History Article published online: 14 April 2023 © 2023. European Society of Gastrointestinal Endoscopy. All rights reserved. Georg Thieme Verlag KG Rüdigerstraße 14, 70469 Stuttgart, Germany
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".