Pelvic Organ Prolapse Mesh Graft Revision Surgery: Rates of Complication by Surgical Approach
Bibliographic record
Abstract
Objective: To determine complication rates after surgery for revision of pelvic organ prolapse mesh grafts, and to compare complication rates by surgical approaches. Methods: A retrospective, population-based cohort study was conducted using the American College of Surgeons National Quality Improvement Program database. Patients undergoing mesh revision by gynecology or urology were classified into vaginal, abdominal, or laparoscopic approaches. Patients having a revision of a midurethral sling were excluded. A multivariable logistic regression analysis was completed. The primary outcome was a composite of surgical complications excluding urinary tract infections. Severe Clavien-Dindo IV complications, rates of readmission, rates of reoperation and urinary tract infections were secondary outcomes. Results: = .052). Compared to a vaginal approach, the abdominal and laparoscopic groups had higher odds of complications (abdominal: adjusted odds ratio [OR] 2.70, 95% confidence interval [CI] 1.29-5.70; laparoscopic: adjusted OR 1.48, 95% CI 0.60-3.68). The abdominal and laparoscopic groups had higher odds of readmission compared to the vaginal group (abdominal: adjusted OR 2.36, 95% CI 0.81-6.90; laparoscopic: adjusted OR 4.12, 95% CI 1.49-11.40). Clavien-Dindo IV complications were rare and only seen after vaginal approach (0.4%, n = 6). Conclusion: Complications were least common after vaginal mesh graft revision surgery, as compared to an abdominal or laparoscopic approach. Serious complications were rare.
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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 | 0.005 |
| 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 | 0.000 |
| 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".