Previously Attempted Large Nonpedunculated Colorectal Polyps Are Effectively Managed by Endoscopic Mucosal Resection
Bibliographic record
Abstract
INTRODUCTION: Endoscopic mucosal resection (EMR) is an effective therapy for naive large nonpedunculated colorectal polyps (N-LNPCPs). The best approach for the treatment of previously attempted LNPCPs (PA-LNPCPs) is undetermined. METHODS: EMR performance for PA-LNPCPs was evaluated in a prospective observational cohort of LNPCPs ≥20 mm. Efficacy was measured by technical success (removal of all visible polypoid tissue during index EMR) and recurrence at first surveillance colonoscopy (SC1). Safety was assessed by clinically significant intraprocedural bleeding, deep mural injury types III-V, clinically significant post-EMR bleeding, and delayed perforation. RESULTS: From January 2012 to October 2019, 158 PA-LNPCPs and 1,134 N-LNPCPs underwent EMR. Median PA-LNPCP size was 30 mm (interquartile range 25-46 mm). Technical success was 93.0% and increased to 95.6% after adjusting for 2-stage EMR. Cold-forceps avulsion with adjuvant snare-tip soft coagulation (CAST) was required for nonlifting polypoid tissue in 73 (46.2%). Median time to SC1 was 6 months (interquartile range 5-7 months). Recurrence occurred in 9 (7.8%). No recurrence was identified among 65 PA-LNPCPs which underwent margin thermal ablation at SC1 vs 9 (18.0%; P < 0.001) which did not. There were significant differences in resection duration (35 vs 25 minutes; P < 0.001), technical success (93.0% vs 96.6%; P = 0.026), and use of CAST (46.2% vs 7.6%; P < 0.001), between PA-LNPCPs and N-LNPCPs. When adjusting for 2-stage EMR, no difference in technical success was identified (95.6% vs 97.8%; P = 0.100). No differences in adverse events or recurrence were identified. DISCUSSION: EMR, using auxillary techniques where necessary, can achieve high technical success and low recurrence frequencies for PA-LNPCPs.
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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 | 0.004 |
| 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".