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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| 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 | 0.001 |
| 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".