Real-life benchmarking bladder cancer care
Bibliographic record
Abstract
INTRODUCTION: Radical cystectomy (RC) is a complex oncological surgical procedure and population studies of routine surgical care have suggested suboptimal results compared to high-volume centers of excellence. A previous Canadian bladder cancer quality-of-care consensus led to adoption of multiple key quality-of-care indicators, with associated benchmarks created using available evidence and expert opinion to inform and measure future performance. Herein, we report real-life benchmark performance for the management of muscle-invasive bladder cancer (MIBC) relative to expert opinion guidance. METHODS: This is a population-based, retrospective, cohort study that used the Ontario Cancer Registry (OCR) to identify all incident patients who underwent RC from 2009-2013. Electronic records of treatment from 1573 patients were linked to OCR; pathology records were obtained for all cases and reviewed by a team of trained data abstractors. The primary objective was to describe benchmarks for identified indicators, first as median values obtained across hospitals or providers, as well as a "pared-mean" approach to identify a benchmark population of "top performance," as defined as the best outcome accomplished for at least 10% of the population. RESULTS: Overall, performance in Ontario across all indicators fell short of expert opinion-determined benchmarks. Annual surgical volume by each surgeon performing a RC (benchmark >6, percent of institutions meeting benchmark=20%), percent of patients with MIBC referred preoperatively to medical oncology (MO; benchmark>90%, percent of institutions meeting benchmark=2%) and radiation oncology (RO; benchmark>50%, percent of institutions meeting benchmark=0%), time to cystectomy within six weeks of transurethral resection of bladder tumor (TURBT) in patients without neoadjuvant chemotherapy (benchmark <6 weeks, percent of institutions meeting benchmark=0%), percent of patients with adequate lymph node dissection (defined as >14 nodes, benchmark>85%, percent of institutions meeting benchmark=0%), percent of patients with positive margins post-RC (benchmark <10%, percent of institutions meeting benchmark=46%), and 90-day mortality (benchmark<5%, percent of institutions meeting benchmark=37%) fell considerably short. Simply evaluating benchmarks across the province as median performance significantly underestimated benchmarks that were possible by top-performing hospitals. CONCLUSIONS: Performance through most bladder cancer quality-of-care indicators fall short of benchmarks proposed by expert opinion. Different methodologies, such as a paredmean approach of top performers, may provide more realistic benchmarking.
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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 | 0.011 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".