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).
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".