The impact of prior urethral sling on artificial urinary sphincter outcomes
Bibliographic record
Abstract
INTRODUCTION: We sought to evaluate device outcomes in men who underwent primary artificial urinary sphincter (AUS) placement after failed male urethral sling (MUS). METHODS: We performed a retrospective chart review of 990 men who underwent an AUS procedure between 2003 and 2014. Of these, 540 were primary AUS placements, including 30 (5.5%) with a history of MUS. AUS revisions and explantations were compared between men stratified by the presence of prior sling. Hazard ratios (HR) adjusting for competing risks were used to determine the association with prior sling and AUS outcomes (infection/erosion, urethral atrophy, and mechanical malfunction), while overall device failure was estimated using Kaplan-Meier and Cox-regression analysis. RESULTS: There was no significant difference in age, body mass index, prior prostatectomy, or pelvic radiation when stratified by history of MUS. However, patients with a history of MUS were more likely to have undergone prior collagen injection (p=0.01). On univariate and multivariate analysis, prior MUS was not associated with device failure (HR 1.54; p=0.27). Three-year overall device survival did not significantly differ between those with and without prior MUS (70% vs. 85%; p=0.21). Also, there were no significant differences in the incidence of device infection/erosion, mechanical malfunction, and urethral atrophy. CONCLUSIONS: AUS remains a viable treatment option for men with persistent or recurrent stress urinary incontinence after MUS. However, while not statistically significant, we identified a trend towards lower three-year device outcomes in patients with prior urethral sling. These findings indicate the need for longer-term studies to determine if slings pose an increased hazard.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".