Between-surgeon variation in 90-day mortality after radical cystectomy for bladder cancer.
Bibliographic record
Abstract
512 Background: Radical cystectomy for bladder cancer is a complex surgical oncology procedure. Given the high degree of skill required to perform radical cystectomy, it is plausible that outcomes may vary among surgeons. We determined whether between-surgeon variation, known as heterogeneity, exists for urologic surgeons practicing at a Canadian academic center. Methods: A retrospective analysis of data from the University of Alberta (UA) Radical Cystectomy Database was performed. Between September 1994 and August 2017, 1031 consecutive patients underwent curative-intent radical cystectomy for histologically proven urothelial carcinoma of the bladder (cTanyN1-3M0) by 1 of 11 urologic surgeons. The main outcome measure was 90-day mortality rate. Multivariable models were used to evaluate heterogeneity in 90-day mortality rate after adjustment for case mix. Statistical tests were two-sided (p≤0.05). Results: Data were evaluable for 1031 patients. There was between-surgeon variation in the 90-day mortality rate (unadjusted range, 0.9% to 13.1%). 3 surgeons had 90-day mortality rates ≤ 3% whereas 5 surgeons had 90-day mortality rates ≥ 5%. Conclusions: A patient’s likelihood of achieving an optimal perioperative outcome differs depending on which urologic surgeon performs his/her radical cystectomy. Research examining the mechanism (s) underlying surgical heterogeneity in perioperative outcome after radical cystectomy is needed.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| 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".