Geometry of cold snare polypectomy and risk of incomplete resection
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 it