Improved artificial urinary sphincter outcomes using a transcorporal cuff placement in patients with a “fragile urethra”
Bibliographic record
Abstract
INTRODUCTION: The artificial urinary sphincter (AUS) is the most effective treatment option for incontinence after prostate cancer treatment. However, patients with a "fragile urethra" (defined as prior radiotherapy, previous failed AUS, or previous urethroplasty) are at increased risk of AUS failure. The aim of this study was to evaluate outcomes using standard and transcorporal cuff placement in this group of patients. METHODS: A retrospective review was performed on patients with a fragile urethra who underwent AUS insertion between 2004 and 2017. The primary outcome was the need for AUS revision. Secondary outcome measures included change in pad use, patient satisfaction, continence (≤1 pad/day), improvement (≥50% change in pad use), and cuff erosion rates. RESULTS: Seventy-six patients met the criteria for inclusion, with a mean age of 71.6 years and a mean followup of 37.9 months. A total of 42.1% had prior radiotherapy, 56.6% had a history of failed AUS, and 19.7% had previous urethroplasty. Transcorporal cuff placement was performed in 31.6% (n=24). These patients had lower revision (20.8% vs. 36.5%; p=0.05) and erosion rates (8.3% vs. 17.3%; p=0.09). There was no significant difference in functional outcomes such as continence (66.7% vs. 73.1%; p=0.57), improvement (100% vs. 90.4%;p=0.17), or satisfaction (82.6% vs. 69.4%; p=0.26), nor for 90-day complications (4.2% vs. 9.6%; p=0.41). CONCLUSIONS: AUS insertion is an effective treatment option for post-prostatectomy incontinence in the setting of a fragile urethra. Transcorporal cuff placement in this subset of patients may be recommended, as it is associated with lower revision and erosion rates compared to standard cuff placement.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".