A7 THE POLYP-BASED RESECT-AND-DISCARD STRATEGY
Bibliographic record
Abstract
Current clinical practice assigns post-polypectomy surveillance intervals based on the number, size and histological aspects of polyps. Our goal was to test a novel polyp-based resect and discard model that assigns surveillance intervals for small polyps based only on size and number of polyps. A post hoc analysis was performed on patients enrolled in a prospective colonoscopy trial. We created a model for polyp-based surveillance interval allocation based on clinical experience for what the most likely pathology-based surveillance interval would be for certain scenarios. The primary outcome was the surveillance interval agreement of the polyp-based resect and discard strategy compared to histopathology and USMSTF based surveillance intervals. Secondary outcomes were the overall reduction in required pathology exams and the number of surveillance intervals that can be provided immediately to patients before leaving the endoscopy unit. In addition, we conducted a medical chart review to assess current clinical practise of surveillance interval guideline adherence at our institution. 457 patients (mean age 62.7, 49.4% female, 514 small polyps, 430 diminutive polyps) were enrolled in the study. When using the polyp-based resect and discard model, the assigned surveillance intervals were correct for 89,3% (95% CI: 0.86–92) of patients when compared to pathology-based surveillance interval assignment. When using the polyp-based model, 88,8% of patients can be provided with immediate surveillance interval recommendations compared to 47,7% when using the pathology-based surveillance interval allocation. When using the polyp-based model, 61.4% of pathology examinations can be omitted. Medical chart review showed that at our institution 43.8% of patients received a correct surveillance interval recommendation. The polyp-based resect and discard model reaches an almost 90% agreement compared to pathology-based surveillance interval allocation recommendations. This alternative model reduces the need for pathology examinations, increases the amount of patients that can be provided with immediate surveillance interval recommendations and has the potential to reduce colonoscopy-associated costs. Clinical adherence to pathology results and guideline recommendations was found to be low, but in the range of what has been previously reported in the literature. Polyp-based resect and discard model None
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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 | 0.011 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".