Procedure Choice in Primary Versus Recurrent Prolapse: A Study of Fellowship-Trained Surgeons
Bibliographic record
Abstract
OBJECTIVE: This retrospective study describes procedures of choice in management of patients with primary prolapse compared with recurrence prolapse patients by fellowship-trained surgeons. METHODS: Surgically managed primary and recurrent prolapse cases from 2012 to 2015 at Houston Methodist Hospital were reviewed. Baseline characteristics, compartment defects, and stage were compared. Mean interval from the index surgeries to management of prolapse recurrence was recorded. In recurrence cases, mesh complaints were noted if present. Primary outcome was the procedure type used to manage cases of recurrence and primary prolapse. Logistic regression was used to determine odds ratio (OR) for the procedure of choice in recurrence and primary repairs of prolapse. RESULTS: Of 386 cases reviewed, 379 met criteria for inclusion; 25.8% of repairs were for recurrence. Recurrence patients were significantly older than primary cases (mean, 63.6 vs 60.5; P = 0.03) and had been postmenopausal for longer (P = 0.004). Median time interval to surgical management of recurrence was 8 years. Thirty percent of recurrence patients treated previously by mesh had mesh complaints. There was no difference in the distribution of defects or stage. Sacrocolpopexy was more frequently used to manage recurrent prolapse (OR, 2.6334; P < 0.0005). Vaginal mesh repairs showed no difference in utilization. Uterosacral ligament fixation (OR, 0.347; P = 0.002) was used more often in primary prolapse. Anterior colporrhaphy (OR, 0.398; P = 0.0005) and uterosacral ligament fixation (OR, 0.347; P = 0.002) were performed less in recurrence cases. CONCLUSION: Fellowship-trained urogynecologists at this institution utilize sacrocolpopexy mesh more frequently in recurrent prolapse, and uterosacral ligament fixation was used more frequently in primary prolapse cases.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".