Incomplete resection rates of 4- to 20-mm non-pedunculated colorectal polyps when using wide-field cold snare resection with routine submucosal injection
Bibliographic record
Abstract
Abstract Background and study aims Incomplete resection of 4- to 20-mm colorectal polyps occur frequently (> 10 %), putting patients at risk for post-colonoscopy colorectal cancer. We hypothesized that routine use of wide-field cold snare resection with submucosal injection (CSP-SI) might reduce incomplete resection rates (IRRs). Patients and methods Patients aged 45 to 80 years undergoing elective colonoscopies were enrolled in a prospective clinical study. All 4- to 20-mm non-pedunculated polyps were resected using CSP-SI. Post-polypectomy margin biopsies were obtained to determine IRRs through histopathology assessment. The primary outcome was IRR, defined as remnant polyp tissue found on margin biopsies. Secondary outcomes included technical success and complication rates. Results A total of 429 patients (median age 65 years, 47.1 % female, adenoma detection rate 40 %) with 204 non-pedunculated colorectal polyps 4 to 20 mm removed using CSP-SI were included in the final analysis. CSP-SI was technical successful in 97.5 % (199/204) of cases (5 conversion to hot snare polypectomy). IRR for CSP-SI was 3.8 % (7/183) (95 % confidence interval [CI] 2.7 %-5.5 %). IRR was 1.6 % (2/129), 16 % (4/25), and 3.4 % (1/29) for adenomas, serrated lesions, and hyperplastic polyps respectively. IRR was 2.3 % (2/87), 6.3 % (4/64), 4.0 % (6/151), and 3.1 % (1/32) for polyps 4 to 5 mm, 6 to 9 mm, < 10 mm, and 10 to 20 mm, respectively. There were no CSP-SI-related serious adverse events. Conclusions Use of CSP-SI results in lower IRRs compared to what has previously been reported in the literature for hot or cold snare polypectomy when not using wide-field cold snare resection with submucosal injection. CSP-SI showed an excellent safety and efficacy profile, however comparative studies to CSP without SI are required to confirm these results.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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".