Full functional-length urethral sphincter- and neurovascular bundle preservation improves long-term continence rates after robotic-assisted radical prostatectomy
Bibliographic record
Abstract
The objective of the study was to test the impact of implementing standard full functional-length urethral sphincter (FFLU) and neurovascular bundle preservation (NVBP) with intraoperative frozen section technique (IFT) on long-term urinary continence in patients undergoing robotic-assisted radical prostatectomy (RARP). We relied on an institutional tertiary-care database to identify patients who underwent RARP between 01/2014 and 09/2019. Until 10/2017, FFLU was not performed and decision for NVBP was taken without IFT. From 11/2017, FFLU and IFT-guided NVBP was routinely performed in all patients undergoing RARP. Long-term continence (≥ 12 months) was defined as the usage of no or one safety- pad. Uni- and multivariable logistic regression models tested the correlation between surgical approach (standard vs FFLU + NVBP) and long-term continence. Covariates consisted of age, body mass index, prostate volume and extraprostatic extension of tumor. The study cohort consisted of 142 patients, with equally sized groups for standard vs FFLU + NVBP RARP (68 vs 74 patients). Routine FFLU + NVBP implementation resulted in a long-term continence rate of 91%, compared to 63% in standard RARP (p < 0.001). Following FFLU + NVBP RARP, 5% needed 1-2, 4% 3-5 pads/24 h and no patient (0%) suffered severe long-term incontinence (> 5 pads/24 h). No significant differences in patient or tumor characteristics were recorded between both groups. In multivariable logistic regression models, FFLU + NVBP was a robust predictor for continence (Odds ratio [OR]: 7.62; 95% CI 2.51-27.36; p < 0.001). Implementation of FFLU and NVBP in patients undergoing RARP results in improved long-term continence rates of 91%.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".