Geometry of cold snare polypectomy and risk of incomplete resection
Bibliographic record
Abstract
Abstract Background Cold snare polypectomy (CSP) is safer than and equally efficacious as hot snare polypectomy (HSP) for the removal of small (<10mm) colorectal polyps. The maximum polyp size that can be effectively managed by piecemeal CSP (p-CSP) without an excessive burden of recurrence is unknown. Methods Resection error risks (RERs), defined as the estimated likelihood of incomplete removal of adenomatous tissue for a single snare resection pass, for CSP and HSP were calculated, based on an incomplete resection rate. Polyp area, snare size, estimated number of resections, and optimal resection defect area were modeled. Overall risk of incomplete resection (RIR) was defined as RIR=1 – (1 – p)n, where p is the RER and n the number of resections. Results A 40-mm polyp has a four times greater area than a 20-mm polyp (314.16mm2 vs. 1256.64mm2), and requires three times more resections (11 vs. 33, respectively, assuming 8-mm piecemeal resection pieces for p-CSP). RIRs for a 40-mm polyp by HSP and p-CSP were 15.1%–23% and 40.74%–60.60% respectively. Conclusion RER is more important with p-CSP than with HSP. The number of resections, n, and consequently RIR increases with increasing polyp size. Given the overwhelming safety of CSP, specific techniques to minimize the RER should be studied and developed.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".