Required withdrawal times to meet adenoma detection targets during colonoscopy
Bibliographic record
Abstract
BACKGROUND AND AIMS: Physicians with baseline adenoma detection rates (ADRs) less than 26% who improve their ADR can reduce patients' risk of postcolonoscopy colorectal cancer. We investigated the average corrected withdrawal time (cWT) required for low-performers (<25% baseline ADR) to achieve an ADR of at least 26%. METHODS: This prospective study used full-length video recordings of elective colonoscopies to determine cWT, ie, withdrawal time adjusted by subtracting the duration of interventions, to better represent mucosal inspection time. The primary outcome was the average cWT required for low performers to achieve an ADR ≥26%. Secondary outcomes included the cWT required for high performers to reach ADR ≥26%, and for both groups to achieve ADR ≥35%. In addition, absolute and incremental increases in ADR, advanced ADR, sessile serrated lesion detection rate, polyp detection rate, polyp and adenomas per colonoscopy per additional cWT, and overall withdrawal time (WT) minute were assessed. RESULTS: In total, 1072 colonoscopies performed by 15 endoscopists were included. Low performers required about 11 minutes of cWT to reach an ADR ≥26% and 11 minutes longer than high performers to achieve ADR ≥35% (2'59″ vs 14'08″). Each additional cWT minute increased adenoma detection odds by 7.1% overall (5.2% for high performers, 15.0% for low performers; P < .001). Absolute ADR gains per cWT minute were 1.1% to 1.3% for high performers and 1.2% to 3.4% for low performers. All detection metrics significantly increased with longer cWT and overall WT. CONCLUSIONS: Longer cWT improves detection across all metrics, with the greatest benefit for low performers. These results support implementation of individualized WT recommendations on the basis of baseline endoscopist performance rather than a universal time threshold.
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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 | 0.004 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".