Urethral lesion by the cuff of the artificial urinary sphincter: A systematic review of optimal management at time of explantation
Bibliographic record
Abstract
Introduction and Objective : Artificial Urinary Sphincter (AUS) is the gold standard for Stress urinary incontinence (SUI) surgical treatment. This systematic review pursues the optimal course of action during AUS cuff explantation due to urethral cuff erosion, the device’s main complication. Methods: Systematic review of Medline, Embase, Cochrane Library, and Scielo databases following the PRISMA statement, from January 2014 to April 2024. The risk of bias was assessed using the “Newcastle–Ottawa Scale for cohorts” and “Critical Appraisal Checklist for Case Series”. Our primary outcome was the stricture rate after treatment. There was no external funding for this review. Results : Of 362 initial studies, six were included, with 277 patients. There were five retrospective single-center studies and one multicentric study. Pelvic irradiation was a risk factor for cuff erosion and stricture formation. Severe lesions (larger than 33% of the urethra) are at higher risk for stricture development and present poor results when treated conservatively. Mild lesions (smaller than 33%) showed no difference between urinary diversion and surgical repair. Conclusions: Mild lesions should be addressed with urethral catheterization for 3-6 weeks associated with a suprapubic tube. Severe erosions should receive surgical correction. Prospective, randomized studies are needed.
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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 | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".