Are urologic surgeons performing robot-assisted radical prostatectomy at the University of Alberta meeting surgical quality performance benchmarks? The PROCURE-02 quality assurance study
Bibliographic record
Abstract
INTRODUCTION: Robot-assisted radical prostatectomy (RARP) is a standard of care primary treatment for men with clinically localized prostate cancer (CLPC). The 2010 Canadian Urological Association (CUA) consensus guideline examining surgical quality performance for radical prostatectomy suggested benchmarks for surgical performance. To date, no study has examined whether Canadian surgeons are achieving these benchmarks. We determined the proportion of University of Alberta (UA) urologic surgeons achieving the CUA surgical quality performance outcome (SQPO) benchmarks. METHODS: A retrospective quality assurance analysis of prospectively collected data from the PROstate Cancer Urosurgery Repository of Edmonton (PROCURE) was performed. Men who underwent RARP for CLPC between September 2007 and May 2018 by one of seven surgeons were analyzed. SQPO were an unadjusted pT2-R1 resection rate <25%, blood transfusion rate <10%, rectal injury rate <1%, and 90-day mortality rate <1%. Descriptive statistics were used to determine the proportion of surgeons achieving the benchmarks. RESULTS: Data were evaluable for 2821 men. Seven of seven (100%) surgeons achieved a blood transfusion rate <10%, rectal injury rate <1%, and 90-day mortality rate <1%. However, only six of seven surgeons achieved an unadjusted pT2-R1 resection rate <25%; one surgeon had an unadjusted pT2-R1 resection rate of 27.9%. Limitations include the lack of centralized pathology review for surgical margin status by a dedicated genitourinary pathologist. CONCLUSIONS: UA surgeons are achieving the CUA SQPO benchmarks for blood transfusion, rectal injury, and perioperative mortality. However, not all UA urologists are achieving a pT2-R1 resection rate <25%. Surgical quality performance initiatives designed to improve cancer control may be warranted.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".