Do larger cuff sizes with artificial urinary sphincter placement increase the risk of leakage after placement?
Bibliographic record
Abstract
INTRODUCTION: To determine whether larger artificial urinary sphincters (AUS) cuff sizes of ≥ 5.0 cm have an impact on urinary incontinence after AUS implantation as compared to cuff sizes ≤ 4.5 cm. MATERIALS AND METHODS: A retrospective chart review of AUS implants performed at our institution from 1991 to 2021. Medical records were reviewed for demographics including body mass index (BMI), cause of incontinence, pelvic radiation, valsalva leak point pressure (VLPP), degree of leakage preoperatively and at 1-year post-AUS surgery, AUS revisions, erosion rate and the need for adjunct medication postoperatively. RESULTS: A total of 110 patients were included in the analysis. Of these, 44 patients had an AUS cuff size of ≥ 5.0 cm and 66 patients had a cuff size ≤ 4.5 cm. After AUS implantation at 1 year both groups had a median pad use of 1 pad per day. Lastly, the erosion rate was higher in the ≤ 4.5 cm cuff group (7.7% vs. 2.4%) but this was not statically significant. In all cases (6 patients) of cuff erosion, each patient had been radiated. CONCLUSION: AUS cuff sizes of ≥ 5.0 cm do not appear to have a negative impact on the degree of incontinence at 1-year post AUS as compared to those with cuff sizes ≤ 4.5 cm. The erosion rate was higher in those with cuffs ≤ 4.5 cm but was not statistically significant. This would suggest that at AUS implantation, the surgeon should choose a larger cuff if there is any doubt especially in those with radiation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.015 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".