Higher surgeon and hospital volume improves long‐term survival after radical cystectomy
Bibliographic record
Abstract
BACKGROUND: Hospital and surgeon (provider) volume are associated with clinically significant outcomes for many types of surgery. Volume-outcome studies in patients undergoing radical cystectomy for bladder cancer have focused primarily on postoperative mortality. In the current study, the authors assessed the effect of cystectomy provider volume on long-term mortality. METHODS: Using administrative databases, 2535 patients who underwent cystectomy by 199 surgeons in 90 hospitals in Ontario, Canada, between 1992 and 2004 were identified. The impact of provider volume on overall survival (OS) was assessed using Cox proportional hazards models fully adjusted for patient and tumor characteristics. Separate models were fit to examine the effect of surgeon and hospital volume. To confirm that the impact of volume on OS was independent of the effect of volume on short-term mortality, analyses were repeated excluding those patients experiencing postoperative deaths. RESULTS: Of 2535 patients, 1796 (70.9%) died during the study period. Both higher hospital volume (hazards ratio [per unit increase in average annual number of procedures], 0.995; 95% confidence interval, 0.990-1.000 [P = .044]) and higher surgeon volume (hazards ratio, 0.984; 95% confidence interval, 0.975-0.994 [P = .002]) were found to be significantly associated with improved OS. Excluding post-operative deaths did not alter the results. Further analyses revealed that the benefit of high volume was attained by receiving care from either high-volume hospitals or high-volume surgeons. CONCLUSIONS: High-volume providers were associated with improved long-term mortality rates compared with low-volume providers. This finding was independent of the effect of volume on perioperative mortality, suggesting that provider volume effects continue to manifest long after surgery.
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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 | 0.005 |
| 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) | 0.001 | 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".