Safety and effectiveness of underwater cold snare resection without submucosal injection of large non-pedunculated colorectal lesions
Bibliographic record
Abstract
Abstract Background and study aims Adverse events are uncommon with cold snaring, but cold techniques are generally reserved for lesions ≤ 9 mm out of concern for incomplete resection or inability to mechanically resect larger lesions. In a non-distended, water-filled lumen, colorectal lesions are not stretched, enabling capture and en bloc resection of large lesions. We assessed the effectiveness and safety of underwater cold snare resection (UCSR) without submucosal injection (SI) of ≥ 10 mm non-pedunculated, non-bulky (≤ 5 mm elevation) lesions with small, thin wire snares. Patients and methods Retrospective analysis of an observational cohort of lesions removed by UCSR during colonoscopy. A single endoscopist performed procedures using a small thin wire (9-mm diameter) cold or (10-mm diameter) hybrid snare. Results Fifty-three lesions (mean 15.8 mm [SD 6.9]; range 10–35 mm) were removed by UCSR from 44 patients. Compared to a historical cohort, significantly more lesions were resected en bloc by UCSR (84.9 % [45/53]; P = 0.04) compared to conventional endoscopic mucosal resection (EMR) (64.0 % [32/50]). Results were driven by high en bloc resection rates for 10– to 19-mm lesions (97.3 % [36/37]; P = 0.01). Multiple logistic regression analysis adjusted for potential confounders showed en bloc resection was significantly associated with UCSR compared to conventional EMR (OR 3.47, P = 0.027). Omission of SI and forgoing prophylactic clipping of post-resection sites did not result in adverse outcomes. Conclusions UCSR of ≥ 10 mm non-pedunculated, non-bulky colorectal lesions is feasible with high en bloc resection rates without adverse outcomes. Omission of SI and prophylactic clipping decreased resource utilization with economic benefits. UCSR deserves further evaluation in a prospective comparative study.
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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 | 0.006 |
| 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".