“Resect and pool”: surveillance interval agreement, safety, and savings from placing all colorectal polyps considered zero risk for cancer in one container for pathologic assessment
Bibliographic record
Abstract
Background: We investigated the effects of collecting all lesions (from all segments) deemed zero risk for cancer in a single bottle for pathology. Methods: We performed two prospective evaluations. In phase 1, resected zero-risk lesions from the same segment were collected in the same bottle. The endoscopist predicted surveillance intervals based on lesion size, number, and histology predictions. Predicted intervals were compared with pathology-based intervals. In phase 2 the "resect and pool" strategy was implemented, in which all zero-risk lesions from all segments were collected in a single bottle. End points were proportion of correctly assigned surveillance intervals, safety (no cancers placed with lesions from other segments), and savings (reduction in pathology bottles and carbon emissions). Results: In phase 1, 3514 lesions were deemed zero risk, and none had cancer. Of 72 non-zero-risk lesions, 6 (8.3%) had cancer. Endoscopist surveillance intervals were correct in 97.2% (95%CI 95.7%-98.2%) of procedures, and 97.1% (95%CI 95.1%-98.4%) when intervals were determined only by lesions from the current colonoscopy. Phase 2 had 5107 zero-risk lesions, and none had cancer. Combining zero-risk lesions from different segments in a single bottle reduced pathology costs and carbon footprint by 62%-64% compared with zero-risk lesions being separated by colorectal segment. Conclusions: When performed by an endoscopist with expertise in optical diagnosis, resect and pool colonoscopy was safe, permitted correct prediction of surveillance intervals, and reduced pathology costs and carbon emissions.
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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 | 0.024 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".