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Record W4407412952 · doi:10.3390/siuj6010011

Therapeutic Options for Advanced Pelvic Organ Prolapse

2025· article· en· W4407412952 on OpenAlexaffvenue
Béatrice Bouchard, Lysanne Campeau

Bibliographic record

VenueSociété Internationale d’Urologie Journal · 2025
Typearticle
Languageen
FieldMedicine
TopicPelvic floor disorders treatments
Canadian institutionsMcGill UniversityJewish General Hospital
Fundersnot available
KeywordsMedicineGeneral surgery

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.471
Threshold uncertainty score0.614

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.029
GPT teacher head0.360
Teacher spread0.331 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations4
Published2025
Admission routes2
Has abstractyes

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