Estimating the optimal perioperative chemotherapy utilization rate for muscle‐invasive bladder cancer
Bibliographic record
Abstract
BACKGROUND: Identifying optimal chemotherapy (CT) utilization rates can drive improvements in quality of care. We report a benchmarking approach to estimate the optimal rate of perioperative CT for muscle-invasive bladder cancer (MIBC). METHODS: The Ontario Cancer Registry and linked treated records were used to identify neoadjuvant and adjuvant CT rates among patients with MIBC during 2004-2013. Monte Carlo simulation was used to estimate the proportion of observed rate variation that could be due to chance alone. The criterion-based benchmarking approach was used to explore whether social and health-system factors were associated with CT rates. We also used the "pared-mean" approach to identify a benchmark population of hospitals with the highest treatment rates. Hospital CT rates were adjusted for case mix and simulated using a multi-level multivariable model and a parametric bootstrapping approach. RESULTS: The study population included 2581 patients; perioperative CT was delivered to 31% (798/2581). Multivariate analysis showed that treatment was strongly associated with patient socioeconomic status and hospital teaching status. The benchmark rate was 36%. Unadjusted CT rates were significantly different across hospitals (range 0%-52%, P < .001). The unadjusted benchmark perioperative CT rate was 45% (95% CI 40%-50%); utilization rate in nonbenchmark hospitals was 28% (95% CI 26%-30%). When using simulated CT rates adjusted for case mix, the benchmark CT rate was 41% (95% CI 35%-47%) and the nonbenchmark hospital CT rate was 30% (95% CI 28%-32%). The simulation analysis suggested that the observed component of variation (38%) was outside the 95% CI (22%-28%) of what could be expected due to chance alone. CONCLUSIONS: There is significant systematic variation in perioperative CT rates for MIBC across hospitals in routine practice. The benchmark perioperative CT rate for MIBC is 36%-41%.
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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 | 0.005 | 0.027 |
| 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 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".