Absorbable Mesh Augmentation Compared With No Mesh for Anterior Prolapse
Bibliographic record
Abstract
OBJECTIVE: To compare anatomical and patient-reported outcomes at 12 months postoperatively for women who had anterior compartment pelvic organ prolapse (POP) surgery using a repair augmented with porcine small intestine submucosa mesh (Mesh Group) compared with those who had a native tissue repair (No Mesh Group). METHODS: This was a randomized controlled trial with 12 months follow-up. The surgical procedure was identical in both groups except for the placement of intervening mesh. The primary outcome was anatomical "cure" (Ba of -1 or less on Pelvic Organ Prolapse Quantification [POP-Q]). Secondary outcomes included POP-Q stage, patient-reported outcomes, and patient satisfaction. The study was powered to detect a 40% difference at 80% power (α=0.05). RESULTS: Fifty-seven women were randomized (28 to Mesh Group, 29 to No Mesh Group). Forty-five (79%) underwent concomitant surgery. At the 12-month follow-up, 56% (15/27) in the Mesh Group and 61% (17/28) in the No Mesh Group were considered cured (relative risk 0.90, 95% confidence interval 0.52-1.54). There were no significant differences between groups in recurrent or persistent prolapse (7% in each group) nor in patient-reported outcomes at 12 months. Pelvic girdle pain occurred in 4 of 27 in the Mesh Group and 3 of 28 in the No Mesh Group. CONCLUSION: No significant differences were observed in anatomical or patient-reported outcomes outcome parameters at 12 months after correction of symptomatic anterior POP by mesh or no mesh repair. In our study, porcine small intestine submucosa mesh did not confer additional benefit over a native tissue repair. CLINICAL TRIAL REGISTRATION: ClinicalTrials.gov, www.clinicaltrials.gov, NCT0095544. LEVEL OF EVEDIENCE: I.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".