Adverse events of cold snare compared with hot snare and ablation endoscopic mucosal resection for large colorectal polyps
Bibliographic record
Abstract
Background: Endoscopic mucosal resection (EMR) techniques for large (≥20 mm) nonpedunculated colorectal polyps (LNPCPs) have expanded with the introduction of ablation and cold EMR. This study assessed adverse events (AEs) for the newer EMR techniques, including cold EMR, compared with hot EMR. Methods: We conducted a secondary analysis of four prospective multicenter studies of consecutive patients with LNPCPs undergoing EMR from 2019 to 2024. The primary outcome was serious AEs (SAEs) with cold and hot EMR. Secondary outcomes included SAEs in the hot EMR subgroups (no ablation [hEMR], margin ablation [hEMR-m], margin and base ablation [hEMR-mb]). Results: 1762 patients (mean age 65.8; 1890 LNPCPs) were included: 522 cold and 1368 hot EMRs (368 hEMR, 770 hEMR-m, 230 hEMR-mb). SAEs were higher with hot EMR (4.7%, 95%CI 3.6%–5.9%) vs. cold EMR (1.9%, 95%CI 0.9%–3.5%), also for the subgroups of hEMR (6.0%, 95%CI 3.8%–8.9%), hEMR-m (3.9%, 95%CI 2.6%–5.5%), and hEMR-mb (5.2%, 95%CI 2.7%–8.9%). Serious postendoscopic bleeding (PEB) was numerically higher with hot EMR (2.3%, 95%CI 1.6%–3.3%) vs. cold EMR (1.3%, 95%CI 0.5%–2.7%), also for the subgroups of hEMR (3.0%, 95%CI 1.5%–5.3%), hEMR-m (1.9%, 95%CI 1.1%–3.2%), and hEMR-mb (2.6%, 95%CI 1.0%–5.6%). Perforation, intraprocedural and post-procedural, was numerically higher with hot EMR (1.2%, 95%CI 0.7%–2.0%) vs. cold EMR (0.2%, 95%CI 0.0%–1.1%). hEMR-m and hEMR-mb with clipping had lower rates of serious and overall PEB than no clipping. Conclusions: Cold EMR demonstrated lower rates of SAEs, serious PEB, and perforation compared with hot EMR. Perforation and mortality occurred almost exclusively after hot EMR. Hot EMR with margin +/− base ablation did not increase SAEs compared with hot EMR without ablation.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".