Factors predicting prolonged operative time for individual surgical steps of robot-assisted radical prostatectomy (RARP): A single surgeon’s experience
Bibliographic record
Abstract
INTRODUCTION: We evaluated the average time required to complete individual steps of robotic-assisted radical prostatectomy (RARP) by an expert RARP surgeon. The intent is to help establish a time-based benchmark to aim for during apprenticeship. In addition, we aimed to evaluate preoperative patient factors, which could prolong the operative time of these individual steps. METHODS: We retrospectively identified 247 patients who underwent RARP, performed by an experienced robotic surgeon at our institution. Baseline patient characteristics and the duration of each step were recorded. Multivariate analysis was performed to predict factors of prolonged individual steps. RESULTS: In multivariable analysis, obesity was a significant predictor of prolonged operative time of: docking (odds ratio [OR] 1.96), urethral division (OR 3.13), and vesico-urethral anastomosis (VUA) (OR 2.63). Prostate volume was also a significant predictor of longer operative time in dorsal vein complex ligation (OR 1.02), bladder neck division (OR 1.03), pedicle control (OR 1.04), urethral division (OR 1.02), and VUA (OR 1.03). A prolonged bladder neck division was predicted by the presence of a median lobe (OR 5.03). Only obesity (OR 2.56) and prostate volume (OR 1.04) were predictors of a longer overall operative time. CONCLUSIONS: Obesity and prostate volume are powerful predictors of longer overall operative time. Furthermore, both can predict prolonged time of several individual RARP steps. The presence of a median lobe is a strong predictor of a longer bladder neck division. These factors should be taken into consideration during RARP training.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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".