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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.004 | 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".