Surgeon Experience and Complications of Transvaginal Prolapse Mesh
Bibliographic record
Abstract
OBJECTIVE: To measure the proportion of women with transvaginal prolapse mesh complications and their association with surgeon volume. METHODS: We conducted a retrospective, population-based cohort study of all women who underwent a mesh-based prolapse procedure using administrative data (hospital procedure and physician billing records) between 2002 and 2013 in Ontario, Canada. The primary outcome was surgical revision of the mesh. Primary exposure was surgeon volume: high (greater than the 75th percentile, requiring a median of five [interquartile range 5-6] procedures per year) and very high (greater than the 90th percentile, requiring a median of 13 [interquartile range 11-14] procedures per year) volume mesh implanters were identified each year. Primary analysis was an adjusted Cox proportional hazards model. RESULTS: A total of 5,488 women underwent mesh implantation by 1 of 368 unique surgeons. Median follow-up time was 5.4 (interquartile range 3.0-8.0) years. We found that 218 women (4.0%) underwent mesh reoperation a median of 1.17 (interquartile range 0.58-2.90) years after implantation. The hazard of reoperation for complications was only lower for patients of very high-volume surgeons (3.0% [145/3,001] compared with 4.8% [73/2,447], adjusted hazards ratio 0.59, 95% confidence interval 0.40-0.86). In multivariable modeling, younger age, concomitant hysterectomy, blood transfusion, and increased medical comorbidity were all associated with vaginal mesh reoperation. CONCLUSION: Approximately 5% of women who underwent mesh-based prolapse surgery required reoperation for a mesh complication within 10 years. The risk of reoperation was lowest for surgeons performing 14 or more procedures per year.
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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 | 0.000 | 0.006 |
| 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 | 0.000 |
| 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".