Association between maximal urethral length preservation and postoperative continence after robot-assisted radical prostatectomy: a meta-analysis and systematic review
Bibliographic record
Abstract
ABSTRACT: Urinary incontinence is a common complication following robot-assisted radical prostatectomy (RARP). Urethral length has been identified as a factor affecting postoperative continence recovery. In this meta-analysis, we examined the association between use of the maximal urethral length preservation (MULP) technique and postoperative urinary continence in patients undergoing RARP. We conducted a comprehensive search of PubMed, Web of Science, Embase, and the Cochrane Library up to December 31, 2023. The quality of the literature was assessed using the Newcastle-Ottawa Scale. A random-effects meta-analysis was performed to synthesize data and calculate the odds ratio (OR) from eligible studies on continence and MULP. Six studies involving 1869 patients met the eligibility criteria. MULP was positively associated with both early continence (1 month after RARP; Z = 3.62, P = 0.003, OR = 3.10, 95% confidence interval [CI]: 1.68-5.73) and late continence (12 months after RARP; Z = 2.34, P = 0.019, OR = 2.10, 95% CI: 1.13-3.90). Oncological outcomes indicated that MULP did not increase the overall positive surgical margin rate or the positive surgical margin status at the prostate apex (both P > 0.05). In conclusion, the use of the MULP technique in RARP significantly improved both early and late postoperative continence outcomes without compromising oncological outcomes.
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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 | 0.009 | 0.022 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.039 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".