A133 MARGIN THERMAL ABLATION WITH SNARE-TIP SOFT COAGULATION EFFECTIVELY MITIGATES RECURRENCE AFTER ENDOSCOPIC MUCOSAL RESECTION OF LARGE NON-PEDUNCULATED COLORECTAL POLYPS
Bibliographic record
Abstract
Abstract Background Recurrence following endoscopic mucosal resection (EMR) historically occurs in approximately 15-20% of large (≥20 mm) non-pedunculated colorectal polyps (LNPCPs). Margin thermal ablation with snare-tip soft coagulation (STSC) of the post-EMR defect is an evidence-based modality to mitigate recurrence. However, international validation of margin thermal ablation outcomes is needed. Aims To evaluate the frequencies of endoscopic and histologic recurrence following margin thermal ablation with STSC for LNPCPs managed by EMR. Methods Consecutive patients ampersand:003E 18 years of age who underwent endoscopic resection for a LNPCP were enrolled in a prospective single center observation cohort study (clinicaltrials.gov ID: NCT05402696). Of those lesions which underwent successful EMR, margin STSC was applied systematically aiming to create at least a 2-3mm rim of completed ablated tissue (complete whitening). Recurrence was evaluated both endoscopically, using a standardized protocol for the post-EMR scar, and histologically. Results From 06/2022-09/2023, 335 LNPCPs were endoscopically resected, including 209 by EMR. Following successful EMR, 182 (87.1%) underwent margin STSC. Of these lesions, 49 LNPCPs in 46 patients were assessed at first surveillance colonoscopy. Margin STSC was complete for 44 (89.8%) lesions and incomplete for 5 (10.2%) due to difficult angulation/positioning (n=3) and ileocecal valve location (n=2). Median interval to first surveillance colonoscopy was 6 (IQR 6-7) months. There was no evidence of recurrence noted on endoscopy. Biopsy was performed in 44 (89.8%) with no evidence of histologic recurrence. Conclusions Thermal ablation of the defect margin with STSC effectively negates recurrence and should be considered standard of care following EMR. Funding Agencies None
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".