A119 THE LOCATION-BASED RESECT AND DISCARD STRATEGY FOR DIMINUTIVE COLORECTAL POLYPS: A PROSPECTIVE CLINICAL STUDY
Bibliographic record
Abstract
Abstract Background Replacing histopathology evaluation of diminutive polyps with optical polyp diagnosis is considered a cost-effective approach. However, the widespread use of optical diagnosis is limited due to concerns about making incorrect optical diagnoses and the requirements of training, credentialing and auditing of performance. Aims This prospective study aimed to evaluate a simplified resect and discard strategy that is not operator dependent. Methods The study evaluated a resect and discard strategy that uses anatomical polyp location to classify colon polyps into non-neoplastic or low-risk neoplastic. All rectosigmoid diminutive polyps were considered hyperplastic and all polyps located proximally to the sigmoid colon were considered neoplastic. Surveillance interval assignments based on these a priori assumptions were compared with those based on actual pathology results and optical diagnosis, respectively. The primary outcome was ≥90% agreement with pathology in surveillance interval assignment. Results Overall, 1117 patients undergoing complete colonoscopy were included and 482 (43.1%) had at least one diminutive polyp. Surveillance interval agreement between the location-based resect and discard strategy and pathological findings using the 2020 US Multi-Society Task Force guideline was 97.0% (95% CI = 0.96 - 0.98), surpassing the ≥90% benchmark. Optical diagnoses using NICE and Sano classifications reached 89.1% and 90.01% agreement, respectively (p <0.0001), and were inferior to the location-based strategy. The location-based resect and discard strategy allowed a 69.7% (95% CI = 0.67 - 0.72) reduction in pathology examinations compared with 55.3% (95% CI = 0.52 - 0.58) (NICE and Sano) and 41.9% (95% CI = 0.39 - 0.45) (WASP) with optical diagnosis. Conclusions The location-based resect and discard strategy achieved very high surveillance interval agreement with pathology-based surveillance interval assignment, surpassing the ≥90% quality benchmark and outperforming optical diagnosis in surveillance interval agreement and the number of pathology examinations avoided. Funding Agencies None
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| 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) | 0.002 | 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".