Effect of radical prostatectomy surgeon volume on complication rates from a large population-based cohort
Bibliographic record
Abstract
INTRODUCTION: Surgical volume can affect several outcomes following radical prostatectomy (RP). We examined if surgical volume was associated with novel categories of treatment-related complications following RP. METHODS: We examined a population-based cohort of men treated with RP in Ontario, Canada between 2002 and 2009. We used Cox proportional hazard modeling to examine the effect of physician, hospital and patient demographic factors on rates of treatment-related hospital admissions, urologic procedures, and open surgeries. RESULTS: Over the study interval, 15 870 men were treated with RP. A total of 196 surgeons performed a median of 15 cases per year (range: 1-131). Patients treated by surgeons in the highest quartile of annual case volume (>39/year) had a lower risk of hospital admission (hazard ratio [HR]=0.54, 95% CI 0.47-0.61) and urologic procedures (HR=0.69, 95% CI 0.64-0.75), but not open surgeries (HR=0.83, 95% CI 0.47-1.45) than patients treated by surgeons in the lowest quartile (<15/year). Treatment at an academic hospital was associated with a decreased risk of hospitalization (HR=0.75, 95% CI 0.67-0.83), but not of urologic procedures (HR=0.94, 95% CI 0.88-1.01) or open surgeries (HR=0.87, 95% CI 0.54-1.39). There was no significant trend in any of the outcomes by population density. CONCLUSIONS: The annual case volume of the treating surgeon significantly affects a patient's risk of requiring hospitalization or urologic procedures (excluding open surgeries) to manage treatment-related complications.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| 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 teacher head, 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".