The Role of Uterine Preservation at the Time of Pelvic Organ Prolapse Surgery
Bibliographic record
Abstract
OBJECTIVE: The aim of the study was to synthesize the current literature and provide surgeons with data to inform counseling of eligible patients for uterine-preserving prolapse surgery (UPPS). METHODS: We compared UPPS to similar techniques incorporating hysterectomy, including native-tissue repairs by vaginal, laparoscopic, or open approach; mesh-reinforced repairs by vaginal, laparoscopic, or open approach; obliterative repairs; and the Manchester procedure. Reviewed outcomes include surgical and patient-reported outcomes, complications, uterine pathology, and sexual function. We conducted a structured literature search of English language articles published 1990-2023, combining MeSH terms for pelvic organ prolapse and UPPS. Data were categorized by procedure and approach, and evaluated to provide recommendations and strength of evidence based on group consensus. RESULTS: Patient counseling on prolapse surgery should follow a benefit/risk assessment related to techniques that preserve the uterus. The discussion should include the benefits of hysterectomy for cancer detection and prevention and acknowledgment that patients should continue cervical cancer screening and evaluation of abnormal uterine bleeding following UPPS. The rate of hysterectomy after UPPS is low and most commonly for recurrent prolapse. If cervical elongation is present, trachelectomy should be considered at the time of UPPS. There is no difference in sexual function between UPPS and prolapse repair with hysterectomy. Data on pregnancy outcomes following UPPS are limited. CONCLUSIONS: Uterine-preserving prolapse surgery should be a surgical option for all patients considering surgical treatment for symptomatic pelvic organ prolapse unless contraindications exist. Uterine-preserving prolapse surgery should be offered using an individualized benefit and risk discussion of both approaches to help patients make an informed decision based on their own values.
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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 | 0.008 | 0.051 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".