Endoscopic marginal assessment of fixed colorectal polyps accurately predicts complete resection after cold snare polypectomy:a prospective single-center observational study
Bibliographic record
Abstract
Objectives This study aimed to ascertain the effectiveness of using fixed cold snare polypectomy (CSP) specimens, accompanied by endoscopic assessment, in predicting complete resection outcomes. Methods In this prospective, single-center, observational investigation, patients with colorectal polyps measuring 5-15 mm were enrolled, who underwent CSP between August 2018 and January 2020. Following resection, the specimens were procured and fixed. The primary focus was on evaluating the accuracy of endoscopic margin appraisal of the fixed specimens in forecasting complete resection. The generalized estimating equation model was employed to delve into the potential risk factors contributing to false-positive endoscopic margin assessments of these fixed specimens. Results A cohort of 150 patients, presenting with 260 polyps, were included in the analysis. The CSP procedure achieved a remarkable complete resection rate of 98.5%. In assessing the accuracy of endoscopic evaluation for complete resection in fixed specimens with negative margins, we observed a sensitivity of 100.0%, specificity of 87.1%, an area under the curve (AUC) of 93.6%, and an overall accuracy of 87.3%. Crucial insights from the multivariate regression analysis unveiled sessile serrated lesions (SSL) as an independent risk factor for generating false-positive results during endoscopic margin assessments of fixed specimens, with an odds ratio of 3.5 (95% CI: 1.3 - 9.3, P = 0.011). Conclusions Endoscopic assessment’s negative lateral margin could accurately predict complete resection in fixed specimens. The fixed specimens of SSL are not suitable for margin assessment by endoscopy after fixation.
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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 | 0.006 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".