Therapeutic Options for Advanced Pelvic Organ Prolapse
Bibliographic record
Abstract
Background: Advanced pelvic organ prolapse (POP) can have a significant impact on women’s health and quality of life (QoL). Several treatments, both conservative and surgical, can be offered to patients. These include vaginal pessaries, abdominal reconstructive surgeries, vaginal reconstruction, as well as obliterative procedures. Methods: This is a narrative review of the management of advanced POP using the PubMed, Google Scholar, and Cochrane databases. Results: Gellhorn pessaries are the most used space-occupying pessaries, with good long-term success rates. The only space-occupying pessaries that allow for self-management by the patient and that could be associated with prolapse reduction are cube pessaries. Laparoscopic sacrocolpopexy (L-SCP) is the gold standard for POP surgery. Other abdominal reconstructive procedures include sacrocervicopexy (SCerP) and laparoscopic lateral suspension (LLS). The two most common vaginal reconstructive techniques are sacrospinous ligament fixation (SSLF) and uterosacral ligament suspension (USLS). Both procedures have comparable success rates. Obliterative procedures include the total, Lefort, and Labhart colpocleisis. These procedures are ideal for women who do not wish to have intercourse or who cannot tolerate extensive surgical procedures. There is a general tendency towards uterine preservation when performing these surgeries. Conclusions: Several therapeutic options exist for advanced POP, and most of them are associated with good long-term success rates. Treatment should be chosen based on patient comorbidities and in the context of shared decision-making.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".