RESECT: A Randomised Controlled Trial of Audit and Feedback in Non–muscle-invasive Bladder Cancer Surgery
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVE: We aimed to determine whether audit, feedback, and education improves surgical performance after transurethral resection of bladder tumour surgery for non-muscle-invasive bladder cancer and as a secondary aim if it reduced recurrence rates. METHODS: This cluster randomised controlled trial compared audit and feedback plus peer comparison and education, with audit alone for four coprimary outcomes: (1) Single-instillation chemotherapy, (2) detrusor muscle sampling, (3) documentation of tumour features, and (4) resection completeness. Early recurrence was a secondary outcome. KEY FINDINGS AND LIMITATIONS: A total of 100 sites were randomised to intervention and 101 to control. In total, 14 915 patients were included. Intervention sites significantly improved documentation of tumour features (adjusted mean difference [95% confidence interval {CI}]: 6.0 [1.8, 10], p = 0.005) and of resection completeness (adjusted mean difference [95% CI]: 5.5 [1.5, 9.5], p = 0.007). There was no statistically significant difference in chemotherapy use (adjusted mean difference [95% CI]: 0.3 [-4.7, 5.3], p = 0.9) or detrusor muscle sampling (adjusted mean difference [95% CI]: 2.6 [-1.3, 6.4], p = 0.2). There was no statistically significant difference in early recurrence rate between arms (adjusted odds ratio [95% CI]: 1.02 [0.8, 1.4], p = 0.9); however, in the control arm, the early recurrence rate reduced compared with baseline (adjusted odds ratio [95% CI]: 0.7 [0.6, 0.9]). CONCLUSIONS AND CLINICAL IMPLICATIONS: Audit and feedback with education improved the documentation of important surgical findings that influence clinical management, but not the performance of detrusor muscle sampling, adjuvant chemotherapy use, or early recurrence rates. Improvements observed in the control arm may explain a lack of effect of the intervention in some outcomes.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.017 | 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".