A Pragmatic Randomized Controlled Trial of an Endoscopist Audit and Feedback Report for Colonoscopy
Bibliographic record
Abstract
INTRODUCTION: Variation in endoscopist performance contributes to poor-quality colonoscopy. Audit and feedback (A/F) can be used to improve physician performance, particularly among lower performing physicians. In this large pragmatic randomized controlled trial, we compared A/F to improve endoscopists' colonoscopy performance to usual practice. METHODS: Endoscopists practicing in Ontario, Canada, in 2014 were randomly assigned in October 2015 (index date) to receive (intervention group, n = 417) or not receive (control group, n = 416) an A/F report generated centrally using health administrative data. Colonoscopy performance was measured in both groups over two 12-month periods: prereport and postreport (relative to the index date). The primary outcome was polypectomy rate (PR). Secondary outcomes were cecal intubation rate, bowel preparation, and premature repeat after normal colonoscopy. A post hoc analysis used adenoma detection rate as the outcome. Outcomes were compared between groups for all endoscopists and for lower performing endoscopists using Poisson regression analyses under a difference-in-difference framework. RESULTS: Among all endoscopists, PR did not significantly improve from prereport to postreport periods for those receiving the intervention (relative rate [RR], intervention vs control: 1.07 vs 1.05, P = 0.09). Among lower performing endoscopists, PR improved significantly (RR, intervention vs control 1.34 vs 1.11, P = 0.02) in the intervention group compared with controls. In this subgroup, adenoma detection rate also improved but not significantly (RR, intervention vs control 1.12 vs 1.04, P = 0.12). There was no significant improvement in secondary outcomes between the intervention and control groups. DISCUSSION: A/F reports for colonoscopy improve performance in lower performing endoscopists (ClinicalTrials.gov: NCT02595775).
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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 | 0.029 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 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".