Surgeon Performance Predicts Early Continence After Robot-Assisted Radical Prostatectomy
Bibliographic record
Abstract
INTRODUCTION: There is limited, yet compelling evidence supporting the role of surgeon technical performance in influencing patient outcomes. To date, this concept has been underexplored in endourologic procedures. We hypothesized that a surgeon's technical performance plays a role in predicting an early return to continence after robot-assisted radical prostatectomy (RARP). MATERIALS AND METHODS: We conducted a retrospective, matched case-control analysis of prospectively collected unedited RARP endoscopic videos performed by a single surgeon. A blinded observer with expertise in intraoperative video analysis evaluated clinically relevant steps of RARP using the global evaluative assessment of robotic skill (GEARS) and the generic error rating tool (GERT). The primary outcome was continence status at 3 months postoperatively, defined as patient use of less than or equal to a single precautionary pad. Mann-Whitney U tests examined differences in predictor variables between cases and controls, and multivariate analysis was conducted using binary logistic regression models. RESULTS: Twenty-four incontinent patients were matched for age, body mass index, preoperative International Prostate Symptoms Score, use of posterior/anterior hitch, prostate weight, and learning curve position. No statistically significant difference in errors between groups was observed using the GERT. On multivariate analysis, overall case GEARS score was independently predictive of 3-month continence status (odds ratios [OR] = 0.55, 95% confidence interval [CI] 0.33-0.91), as were urethrovesical anastomosis (OR = 0.70, 95% CI 0.50-0.97) and bladder neck GEARS scores (OR = 0.69, 95% CI 0.51-0.94). CONCLUSIONS: Our study generates the hypothesis that there may be a link between surgeon technical performance and functional outcomes in RARP. This relationship may have implications for the accreditation and training of future urologists and warrants further investigation.
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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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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 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".