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Laparoscopic repair of vaginal vault prolapse

2003· review· en· W2022314068 on OpenAlexaff
Arnaud Wattiez, Roy Mashiach, Manuel Donoso

Bibliographic record

VenueCurrent Opinion in Obstetrics & Gynecology · 2003
Typereview
Languageen
FieldMedicine
TopicPelvic floor disorders treatments
Canadian institutionsHotel Dieu Hospital
Fundersnot available
KeywordsMedicineVaginal Vault ProlapseUterosacral ligamentFasciaDissection (medical)Vaginal vaultSurgeryUterine prolapseSurgical meshPelvic floorVault (architecture)General surgeryVaginaHernia

Abstract

fetched live from OpenAlex

PURPOSE OF REVIEW: Currently, the two laparoscopic techniques available and described in the literature for the treatment of vaginal vault prolapse are uterosacral ligament vault suspension and sacrocolpopexy. These two techniques are opposing each other fundamentally. While the first is reconstructive, the second is essentially palliative. RECENT FINDINGS: In both methods the surgeon starts with the identification and dissection of the pubocervical and rectovaginal fascia. In the first technique however, the new vaginal vault, made by re approximation of the two fasciae, is attached to the uterosacral complex, while in the second one, each fascia is suspended from the sacral promontorium, using a mesh. In review of the existing literature, it seems that the palliative surgical approach is more successful in the long term, giving a cure rate of approximately 92%, probably as it involves using mesh instead of the native tissue. SUMMARY: In this article we discuss the laparoscopic techniques available currently, analyse their results, discuss their differences and compare them with other non-laparoscopic techniques. Finally, we discuss the different options described, and offer some guidelines for the future of laparoscopic treatment of pelvic prolapse.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.006
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0060.002

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 head0.090
GPT teacher head0.400
Teacher spread0.310 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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

Citations51
Published2003
Admission routes1
Has abstractyes

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