Local recurrence rates after resection of large colorectal serrated lesions with or without margin thermal ablation
Bibliographic record
Abstract
Introduction Serrated lesions (SLs) including traditional serrated adenomas (TSA), large hyperplastic polyps (HP) and sessile serrated lesions (SSLs) are associated with high incomplete resection rates. Margin ablation combined with EMR (EMR-T) has become routine to reduce local recurrence while cold snare polypectomy (CSP) is becoming recognized as equally effective for large SLs. Our aim was to evaluate local recurrence rates (LRR) and the use of margin ablation in preventing recurrence in a retrospective cohort study.Methods Patients undergoing resection of ≥15 mm colorectal SLs from 2010-2022 were identified through a pathology database and electronic medical records search. Hereditary CRC syndromes, first follow-up > 18 months or no follow-up, surgical resection were excluded. Primary outcome was LRRs (either histologic or visual) during the first 18-month follow-up. Secondary outcomes were LRRs according to size, and resection technique.Results 191 polyps in 170 patients were resected (59.8% women; mean age, 65 years). The mean size of polyps was 22.4 mm, with 107 (56.0%) ≥20 mm. 99 polyps were resected with EMR, 39 with EMR-T, and 26 with CSP. Mean first surveillance was 8.2 mo. Overall LRR was 18.8% (36/191) (16.8% for ≥20 mm, 17.9% for ≥30 mm). LRR was significantly lower after EMR-T when compared with EMR (5.1% vs. 23.2%; p = 0.013) or CSP (5.1% vs. 23.1%; p = 0.031). There was no difference in LRR between EMR without margin ablation and CSP (p = 0.987).Conclusion The local recurrence rate for SLs ≥15 mm is high with 18.8% overall recurrence. EMR with thermal ablation of the margins is superior to both no ablation and CSP in reducing LRRs.
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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 | 0.001 | 0.003 |
| 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".