Long-Term Outcomes After Vaginal and Laparoscopic Mesh Hysteropexy for Uterovaginal Prolapse: A Parallel Cohort Study (eVAULT)
Bibliographic record
Abstract
IMPORTANCE: Data on long-term mesh hysteropexy outcomes are limited. This study provides 7-year data from the original VAULT (Vaginal and Laparoscopic Mesh Hysteropexy for Uterovaginal Prolapse Trial) study. OBJECTIVE: The aim of this study was to compare long-term outcomes and success for laparoscopic sacral hysteropexy (LSHP) and vaginal mesh hysteropexy (VMHP). STUDY DESIGN: This multicenter, prospective parallel cohort was an extension to the initial VAULT study. Subjects were contacted, and informed consent was obtained. We collected baseline demographics and the latest Pelvic Organ Prolapse-Quantification examination data from chart review and conducted telephone interviews to update demographic information and collect Pelvic Floor Distress Inventory Short-Form, Patient Global Impression of Improvement, prolapse reoperation/pessary use, and complications. Surgical success was defined as no bulge symptoms, satisfaction score of "very much better" or "much better," and no reoperation/pessary use. RESULTS: Five of 8 original sites enrolled 53 subjects (LSHP n = 34 and VMHP n = 19). The LSHP group was younger (67 vs 74, P < 0.01), but there were no differences in parity, body mass index, menopause, race, insurance, tobacco use, or Charlson Comorbidity Index. The median subjective follow-up was 7.3 ± 0.9 years. Composite success was 82% LSHP versus 74% VMHP. Pelvic Floor Distress Inventory Short-Form composite scores were similar at baseline and improved for both groups (P < 0.01) with lower bother observed in the LSHP group (20.8 vs 43.8, P = 0.01). There were no differences in complications. CONCLUSIONS: Over 7 years after surgery, LSHP and VMHP have high success, low retreatment, and low complication rates that did not differ between groups. Although there is a trend toward better anatomic support in the LSHP group, these findings were not significant and we are underpowered to detect a difference.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".