A118 POLYP SIZE CUT-OFF LEVEL TO IMPLEMENT OPTICAL POLYP DIAGNOSIS
Bibliographic record
Abstract
Abstract Background Optical polyp diagnosis can be used for real-time pathology prediction of colorectal polyps ≤10 mm. However, the risk of misdiagnosing a polyp with advanced pathology potentially increases with increasing polyp size. Aims This study aimed to evaluate different size cut-offs for using optical polyp diagnosis and the associated risk of patients undergoing inadequate follow-up or surveillance. Methods In a post-hoc analysis of two prospective studies, the performance of optical diagnosis was evaluated in three polyp size groups: 1–3 mm, 1–5 mm, and 1–10 mm. The primary outcome was the proportion of patients with advanced adenomas and delayed or inappropriate surveillance. Secondary outcomes included percentage of polyps with advanced pathology, agreement between surveillance intervals based on high-confidence optical diagnosis and pathology outcomes, reduction in histopathological examinations, and proportion of patients who could receive an immediate surveillance interval recommendation. Results We included 1525 patients with complete colonoscopies (mean age 62.9 years, 50.2% male). The percentage of patients with advanced adenomas and delayed or inappropriate surveillance was 0.7%, 1.7%, and 1.8% when using optical diagnosis for patients with polyps of 1–3, 1–5, and 1–10 mm, respectively. The percentage of polyps with advanced pathology was 0.5%, 1.4%, and 1.9%, respectively. Surveillance interval agreement between pathology and optical diagnosis was 99%, 98%, and 97.8%, respectively. Total reduction in pathology examinations was 33.9%, 53.5%, and 69.0%, respectively. Conclusions A 3-mm cut-off for clinical implementation of optical polyp diagnosis yielded high surveillance interval agreement with pathology and a high reduction in pathology examinations while minimizing the risk of inappropriate management for polyps with advanced pathology. Funding Agencies 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 | 0.010 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.003 | 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".