Piecemeal cold snare polypectomy versus conventional endoscopic mucosal resection for large sessile serrated lesions: a retrospective comparison across two successive periods
Bibliographic record
Abstract
OBJECTIVE: Large (≥20 mm) sessile serrated lesions (L-SSL) are premalignant lesions that require endoscopic removal. Endoscopic mucosal resection (EMR) is the existing standard of care but carries some risk of adverse events including clinically significant post-EMR bleeding and deep mural injury (DMI). The respective risk-effectiveness ratio of piecemeal cold snare polypectomy (p-CSP) in L-SSL management is not fully known. DESIGN: Consecutive patients referred for L-SSL management were treated by p-CSP from April 2016 to January 2020 or by conventional EMR in the preceding period between July 2008 and March 2016 at four Australian tertiary centres. Surveillance colonoscopies were conducted at 6 months (SC1) and 18 months (SC2). Outcomes on technical success, adverse events and recurrence were documented prospectively and then compared retrospectively between the subsequent time periods. RESULTS: A total of 562 L-SSL in 474 patients were evaluated of which 156 L-SSL in 121 patients were treated by p-CSP and 406 L-SSL in 353 patients by EMR. Technical success was equal in both periods (100.0% (n=156) vs 99.0% (n=402)). No adverse events occurred in p-CSP, whereas delayed bleeding and DMI were encountered in 5.1% (n=18) and 3.4% (n=12) of L-SSL treated by EMR, respectively. Recurrence rates following p-CSP were similar to EMR at 4.3% (n=4) versus 4.6% (n=14) and 2.0% (n=1) versus 1.2% (n=3) for surveillance colonoscopy (SC)1 and SC2, respectively. CONCLUSIONS: In a historical comparison on the endoscopic management of L-SSL, p-CSP is technically equally efficacious to EMR but virtually eliminates the risk of delayed bleeding and perforation. p-CSP should therefore be considered as the new standard of care for L-SSL treatment.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".