Evaluation of the Effect of Hospital and Physician Factors on Likelihood of Revision After Mid-Urethral Sling Placement
Bibliographic record
Abstract
Objective: To estimate rates of revision surgery after insertion of mesh midurethral slings (MUS) and explore if healthcare attributes such as physician specialty, annual operative volume, or hospital type are risk factors for this outcome. Methods: This study used a population-based retrospective cohort of women who underwent MUS insertion over a 13-year interval (2004–2017) in Alberta, Canada. The main outcome was subsequent surgery for revision of MUS, defined by a composite of surgical procedures. Exposures included annual number of MUS procedures performed by the surgeon, facility type, surgeon specialty, patient age, and concomitant prolapse repair. Mixed-effects logistic regression utilizing linear spines was used to test the a priori hypothesis that annual surgical volume would be inversely related in a non-linear fashion to risk of revision. Results: In a cohort of 19,511 women, cumulative rates of revision surgery were 3.36% (95% CI 3.06–3.68) at 5 years and 4.57% (95% CI 4.00–5.21) at 10 years. The first year after MUS insertion was the most vulnerable window, with 0.39% (95% CI 0.31–0.49) undergoing revision within 30 days and 2.05% (95% CI 1.85–2.26) within a year. Concomitant prolapse repairs (OR = 1.24, 95% CI 1.04–1.48) and surgeon’s annual volume were associated with revision. After 50 cases per year, odds of revision declined with each additional case (OR = 0.991 per case, 95% CI 0.983–0.999; OR = 0.91 per 10 cases, 95% CI 0.84–0.98) and plateaued at 110 cases per year. Surgeon specialty, hospital type, and patient age were not associated with outcome. Conclusions and relevance: Within 10 years, nearly 1 in 20 women underwent revision surgery after MUS insertion. Physician annual surgical volume appears to be a risk factor, with a decline in risk of revision surgery occurring at an annual threshold of >50 cases. Given that annual case volume is a potentially modifiable risk factor, development of policies regarding minimum caseload parameters for surgeons performing MUS procedures may hold potential to improve the quality of MUS surgery.
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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 | 0.004 | 0.022 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".