Performance report cards increase adenoma detection rate
Bibliographic record
Abstract
Abstract Background and study aims Adenoma detection rate (ADR) is an important measure of colonoscopy quality, as are polyp, advanced ADR, and adenocarcinoma detection rates. We investigated whether performance report cards improved these outcome measures. Patients and methods Endoscopists were given report cards comparing their detection rates to the institutional mean on an annual basis. Detection rates were evaluated at baseline, 1 year after report cards (Year 1), and 2 years after report cards (Year 2). Endoscopists were unaware of the study and received no other interventions. The primary outcome was ADR and secondary outcomes were polyp detection rate (PDR), advanced ADR, and adenocarcinoma detection rate. Multivariate regression was performed to adjust for temporal trends in patient, endoscopists, and procedural factors. Results Seventeen physicians performed 3,118 screening colonoscopies in patients with positive FOBT or family history of colon cancer. The ADR increased from 34.5 % (baseline) to 39.4 % (Year 1) and 41.2 % (Year 2) (P = 0.0037). The PDR increased from 45 % (baseline) to 48.8 % (Year 1) and 51.8 % (Year 2) (P = 0.011). There was no significant improvement in advanced ADR or adenocarcinoma detection rates. On multivariate analysis, the ADR increased by 22 % in Year 1 (P = 0.03) and 30 % in Year 2 (P = 0.008). Among physicians with a baseline ADR < 25 %, improvement in ADR was even greater, increasing 2.2 times by the end of the study (P = 0.004). Improvements in ADR were not correlated with specialty although gastroenterologists were 52 % more likely to find an adenoma than general surgeons. Conclusions Annual performance report cards increased adenoma detection rates, especially among physicians with low ADR < 25 %.
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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 | 0.006 | 0.057 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".