Association between surgical case volume and survival in T1 bladder cancer: A plea for regionalization of care?
Bibliographic record
Abstract
Introduction: Prior research demonstrated an association between surgical case volume and survival in muscle-invasive bladder cancer (BC). This relationship, however, has not been investigated in the setting of T1 BC so far. Therefore, we investigated whether a higher surgical case volume of T1 BC translates into improved survival outcomes. Methods: Province-wide pathology reports (January 2002 to December 2015) were linked with health administrative data to identify patients diagnosed with T1 BC. For each patient, we determined the T1 case volume of the involved surgeon by benchmarking (percentile) her/him against his/her colleagues during a lookback period of one year. The volume-outcome (overall survival) relationship was then investigated by Cox proportional hazards regression (unadjusted and adjusted for a wide range of assumed confounders) that incorporated volume in three different ways (80th percentile and higher vs. below, median and higher vs. below, continuous [quintiles]). Effect sizes were presented as hazard ratios (95% confidence interval). Results: We identified 7426 patients who were diagnosed with T1 BC and followed for 4.8 years. A third of all patients (n=1895, 25.5%) received surgery by a high-volume surgeon (80th percentile and higher). Higher T1 case volume was associated with improved survival both in unadjusted (80th percentile: 0.93 [0.86–0.99]; median: 0.93 [0.87–0.99]; continuous: 0.97 [0.94–0.99]) and adjusted analysis (80th percentile: 0.94 [0.88–1.01]; median: 0.93 [0.87–0.99]; continuous: 0.97 [0.95–0.99]). Conclusions: This province-wide cohort study could demonstrate a volume-outcome relationship in T1 BC and raises questions regarding the regionalization of care in high-risk non-muscle-invasive BC. The generalizability of our findings, however, is limited by the fact that the performance of the initial resection by a high-volume surgeon does not necessarily translate into downstream care by the same surgeon.
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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 | 0.002 | 0.014 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".