Does intraoperative difficulty at time of robotic-assisted radical prostatectomy predict urinary continence recovery?
Bibliographic record
Abstract
INTRODUCTION: Several studies have reported the preoperative and intraoperative predictors of urinary continence after robotic-assisted laparoscopic radical prostatectomy (RARP). No studies have addressed the impact of surgeon satisfaction and perceived surgical difficulty on continence recovery after RARP. METHODS: We conducted a retrospective study of prospectively collected data for patients treated with RARP for clinically organ-confined prostate cancer. Perioperative variables were recorded and studied. Patients were followed with regular visits at one, three, six, 12, and 24 months after surgery. The primary endpoint of the study was time to continence. RESULTS: A total of 322 patients treated with RARP were included. At least 80% of patients had 24-month postoperative continence followup. Continence rates were 39.1, 58.2, 71.1, 80.9, and 90.7% at one, three, six, 12, and 24 months, respectively. Perceived intermediate and high difficulty cases were associated with lower hazards of continence after RARP compared to low-difficulty cases (hazard ratio [HR] intermediate vs. low: 0.63, p=0.006; HR high vs. low: 0.52, p<0.001). Similarly, increased prostate size and decreased operative time were associated with low hazard of continence after RARP. Conversely, no statistically significant differences were recorded for surgeon satisfaction and preoperative Sexual Health Inventory for Men score (all p>0.05) at multivariate analysis. CONCLUSIONS: Overall difficulty encountered by the surgeon at time of RARP is an independent predictor of continence recovery, in addition to prostate size and preoperative International Prostate Symptoms Score. Predictive preoperative factors for difficult surgery should be dealt with by an experienced surgeon to hasten continence recovery after surgery.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".