Impact of preoperative and postoperative membranous urethral length measured by 3 Tesla magnetic resonance imaging on urinary continence recovery after robotic-assisted radical prostatectomy
Bibliographic record
Abstract
INTRODUCTION: We sought to investigate the impact of preoperative and postoperative membranous urethral length (MUL) on urinary continence using 3 Tesla (3T) magnetic resonance imaging (MRI) after robotic-assisted radical prostatectomy (RARP). METHODS: Between 2008 and 2013, 190 men with RARP underwent preoperative and postoperative MRI. Patients who received adjuvant radiotherapy or who were lost to followup were excluded, leaving 186 patients eligible for analysis. Preoperative MUL was estimated from the prostate apex to the penile bulb, while postoperative MUL was estimated from the bladder neck to penile bulb. Patients with no pads or protection were considered to have complete continence. Logistic regression analysis was used to identify predictors associated with urinary incontinence at six and 12 months. RESULTS: Age was commonly associated with urinary incontinence at six and 12 months. In addition, diabetes mellitus (DM) was another factor associated with urinary incontinence at 12 months. When adjusting these variables, preoperative MUL ≤16 mm (95% confidence interval [CI] 1.01-1.14; p=0.022), postoperative MUL ≤14 mm (95% CI 1.16-9.80; p=0.025) and percent change of MUL >18% (95% CI 1.17-7.23; p=0.021) were significantly associated with urinary incontinence at six months. However, at 12 months, preoperative MUL ≤13.5 mm (95% CI 1.85-19.21; p=0.003) and postoperative MUL ≤13 mm (95% CI 1.24-13.84; p=0.021) had impacts on urinary incontinence, but not percent change of MUL. CONCLUSIONS: Preoperative and postoperative MUL were significantly associated with urinary continence recovery after RARP. Therefore, efforts to preserve MUL are highly recommended during surgery for optimal continence outcomes after RARP.
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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 | 0.000 | 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) | 0.001 | 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".