250 Single Perineal Incision for Artificial Urinary Sphincter: An Analysis of Technique, Outcomes, and Experience
Bibliographic record
Abstract
Artificial urinary sphincter (AUS) placement is the gold standard for the treatment of severe post-prostatectomy urinary incontinence. Various surgical approaches of implantation have been described (perineal vs. penoscrotal), but both techniques use a traditional counter incision for regulating balloon placement. We present our experience with AUS implantation, which examines a large series of patients who underwent a minimally invasive single perineal incision AUS placement approach. Data was collected from all men undergoing artificial urinary sphincter placement by a single high-volume surgeon over an 18-year period of time (2000-2018). Demographic data and outcomes data related to sphincter placement was recorded from electronic medical records, which included subjective histories and questionnaires. Institutional ethics approval was received. A total of 145 AUS were placed over the study period. Of these, 84 were completed via a single perineal incision for both device and regulating balloon placement. Almost all 81 (96%) complained of pre-operative leakage of more than 3 pads per day. Post-operatively, 75% were satisfied with their continence, with 21 (25%) complaining of recurrent incontinence. A total of 5 (6%) patients had a post-operative infection, 10 (12%) patients had device erosion and 11 (13%) had a device malfunction, but only 3 (4%) had regulating balloon related dysfunction. A total of 24 (29%) patients required revision of their device at median of 20 months (IQR 6-32.5 months).
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".