Complications and Interventions in Patients with an Artificial Urinary Sphincter: Long-Term Results
Bibliographic record
Abstract
PURPOSE: The artificial urinary sphincter is a common treatment of male urinary incontinence. We sought to characterize long-term rates of artificial urinary sphincter revision/removal and reimplantation, and associated risk factors. MATERIALS AND METHODS: We performed a population based, retrospective study using the Ontario Health Insurance Plan database of all male patients who underwent artificial urinary sphincter implantation from 1994 to 2013. Hospital, diagnostic and billing codes were used to identify patients. The Kaplan-Meier method and multivariable Cox proportional hazards models were applied to examine the cumulative incidence of artificial urinary sphincter reimplantation and revision/removal, and identify risk factors, respectively. RESULTS: A total of 1,632 male patients underwent initial implantation of an artificial urinary sphincter. The 10-year revision/removal and reimplantation rates were 34% and 27%, respectively. There was no difference between high and low volume hospitals or between community and academic hospitals in terms of revision/removal. A high level of comorbidity was associated with an increasing risk of removal/revision. The reimplantation rate was significantly lower in patients who underwent insertion at hospitals with the highest volume of surgery (HR 0.55, 95% CI 0.37-0.82, p <0.01). A high level of comorbidity was not associated with an increasing risk of reimplantation. Preimplantation radiotherapy was not significantly associated with the risk of reimplantation (p = 0.17) or revision/removal (p = 0.95). Other factors were not significantly associated with reimplantation or revision/removal. CONCLUSIONS: Most men who undergo artificial urinary sphincter placement still have a device without repeat surgery 10 years following insertion. Radiotherapy does not increase the risk of repeat surgery. A high level of comorbidity was associated with an increasing risk of removal/revision.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".