Treatment of urethral stricture disease in women: A multi‐institutional collaborative project from the SUFU research network
Bibliographic record
Abstract
AIM: Female urethral stricture disease is rare and has several surgical approaches including endoscopic dilations (ENDO), urethroplasty with local vaginal tissue flap (ULT) or urethroplasty with free graft (UFG). This study aims to describe the contemporary management of female urethral stricture disease and to evaluate the outcomes of these three surgical approaches. METHODS: This is a multi-institutional, retrospective cohort study evaluating operative treatment for female urethral stricture. Surgeries were grouped into three categories: ENDO, ULT, and UFG. Time from surgery to stricture recurrence by surgery type was analyzed using a Kaplan-Meier time to event analysis. To adjust for confounders, a Cox proportional hazard model was fit for time to stricture recurrence. RESULTS: Two-hundred and ten patients met the inclusion criteria across 23 sites. Overall, 64% (n = 115/180) of women remained recurrence free at median follow-up of 14.6 months (IQR, 3-37). In unadjusted analysis, recurrence-free rates differed between surgery categories with 68% ENDO, 77% UFG and 83% ULT patients being recurrence free at 12 months. In the Cox model, recurrence rates also differed between surgery categories; women undergoing ULT and UFG having had 66% and 49% less risk of recurrence, respectively, compared to those undergoing ENDO. When comparing ULT to UFG directly, there was no significant difference of recurrence. CONCLUSION: This retrospective multi-institutional study of female urethral stricture demonstrates that patients undergoing endoscopic management have a higher risk of recurrence compared to those undergoing either urethroplasty with local flap or free graft.
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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 | 0.007 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".