Postoperative Non-Surgical Interventions to Improve Urinary Continence After Robot-Assisted Radical Prostatectomy: A Systematic Review
Bibliographic record
Abstract
Background The occurrence of postoperative urinary incontinence (UI) remains a problem for patients undergoing robot-assisted radical prostatectomy (RARP). Non-surgical interventions (NSI) in addition to intraoperative techniques and patient behavioral changes have been proposed to improve urinary continence (UC) recovery after RARP. However, to date, the real clinical impact of postoperative NSI remains not well characterized. Materials and Methods We performed a Systematic Review in April 2021, using Allied and Complementary Medicine (AMED), Embase, and MEDLINE according to the PRISMA recommendations and using the Population, Intervention, Comparator and Outcome (PICO) criteria. Primary outcome of interest was the impact of NSI on UC recovery rate and time to achieve UC after RARP. Secondary outcomes of interest were the assessment of patient adherence to NSI, risk factors associated with UI, and correlation between postoperative NSI and sexual activity recovery. Results A total of 2758 articles were screened, and 8 full texts including 1146 patients were identified (3 randomized controlled trials, 3 prospective single-arm trials, and 2 retrospective series). Postoperative NSI of interest included pelvic floor muscle training (PFMT) (n = 6 studies) and administration of oral medications (solifenacin) (n = 2 studies). PFMT appeared to increase UC rates and to accelerate time to achieve UC in the early postoperative period. Similarly, solifenacin provided higher rates of UC recovery and contributed to a certain degree of symptomatic relief. There was a great variability regarding NSI features and data reporting among studies. Major limitations were the small sample sizes and the short follow-up. Conclusion Postoperative NSI to manage UI after RARP include PFMT and solifenacin administration. Both seem to modestly improve early UC recovery. Nonetheless, evidence supporting their routinely use is still weak and lacks appropriate follow-up to evaluate possible benefits on long-term UC recovery.
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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 | 0.005 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".