Impact of provider volume on operative mortality after radical cystectomy in a publicly funded healthcare system
Bibliographic record
Abstract
INTRODUCTION: We assess the effect of cystectomy provider volume on postoperative mortality in a publicly funded healthcare system. Hospital and surgeon (provider) volume have been shown to be associated with clinically important outcomes for many types of surgery. Volume-outcome studies in patients undergoing radical cystectomy for bladder cancer have primarily originated from privately funded healthcare systems. METHODS: We identified patients undergoing cystectomy in Ontario, Canada, between 1992 and 2004 using administrative databases. The effect of provider volume on postoperative mortality was assessed with multilevel (hierarchical or random effects) logistic regression models, adjusted for patient characteristics. Separate models were fit to examine the effect of surgeon volume and the effect of hospital volume. RESULTS: Of the 3296 cystectomy patients identified, 126 (3.8%) experienced a postoperative death. Neither hospital volume (odds ratio [per 1 unit increase in volume] 0.98, 95% confidence interval [CI] 0.95-1.00; p = 0.074) nor surgeon volume (odds ratio 0.96, 95% CI 0.90-1.02; p = 0.143) were statistically significantly associated with postoperative cystectomy mortality. CONCLUSIONS: In Ontario's publicly funded healthcare system, provider volume was not significantly associated with postoperative mortality.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".