Assessing the efficacy of pelvic floor muscle training and duloxetine on urinary continence recovery following radical prostatectomy: A randomized clinical trial
Bibliographic record
Abstract
BACKGROUND: Urinary incontinence (UI) can negatively impact quality of life (QoL) after robot-assisted radical prostatectomy (RARP). Pelvic floor muscle training (PFMT) and duloxetine are used to manage post-RARP UI, but their efficacy remains uncertain. We aimed to investigate the efficacy of PFMT and duloxetine in promoting urinary continence recovery (UCR) after RARP. METHODS: A randomized controlled trial involving patients with urine leakage after RARP from May 2015 to February 2018. Patients were randomized into 1 of 4 arms: (1) PFMT-biofeedback, (2) duloxetine, (3) combined PFMT-biofeedback and duloxetine, (4) control arm. PFMT consisted of pelvic muscle exercises conducted with electromyographic feedback weekly, for 3 months. Oral duloxetine was administered at bedtime for 3 months. The primary outcome was prevalence of continence at 6 months, defined as using ≤1 security pad. Urinary symptoms and QoL were assessed by using a visual analogue scale, and validated questionnaires. RESULTS: From the 240 patients included in the trial, 89% of patients completed 1 year of follow-up. Treatment compliance was observed in 88% (92/105) of patients receiving duloxetine, and in 97% (104/107) of patients scheduled to PFMT-biofeedback sessions. In the control group 96% of patients had achieved continence at 6 months, compared with 90% (p = 0.3) in the PMFT-biofeedback, 73% (p = 0.008) in the duloxetine, and 69% (p = 0.003) in the combined treatment arm. At 6 months, QoL was classified as uncomfortable or worse in 17% of patients in the control group, compared with 44% (p = 0.01), 45% (p = 0.008), and 34% (p = 0.07), respectively. Complete preservation of neurovascular bundles (NVB) (OR: 2.95; p = 0.048) was the only perioperative intervention found to improve early UCR. CONCLUSIONS: PFMT-biofeedback and duloxetine demonstrated limited impact in improving UCR after RP. Diligent NVB preservation, along with preoperative patient and disease characteristics, are the primary determinants for early UCR.
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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 | 0.006 | 0.006 |
| 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 | 0.001 |
| 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".