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Record W2981177173 · doi:10.1093/asjof/ojz030

Posterior Vaginoplasty With Perineoplasty: A Canadian Experience With Vaginal Tightening Surgery

2019· article· en· W2981177173 on OpenAlexaffabout
Ryan E Austin, Frank Lista, Peter-George Vastis, Jamil Ahmad

Bibliographic record

VenueAesthetic Surgery Journal Open Forum · 2019
Typearticle
Languageen
FieldMedicine
TopicFemale Genital Mutilation/Cutting Issues
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMedicineVaginoplastySurgeryVaginaPerineumPerioperative

Abstract

fetched live from OpenAlex

Following vaginal trauma, most commonly vaginal delivery, women may experience vaginal laxity as a result of local tissue stretching and separation of the pelvic floor musculature. In addition to this generalized sensation of laxity, women may complain of decreased sexual satisfaction, gaping of the perineum, and excessive vaginal secretions. Since 2014, the authors have used a posterior vaginoplasty with perineoplasty technique for the surgical management of vaginal laxity. To date, the authors have performed surgical vaginal tightening in 30 consecutive patients and found that the posterior vaginoplasty with perineoplasty technique has allowed us to achieve reproducible outcomes with no postoperative complications. This article will review the authors' approach to patients presenting for surgical vaginal tightening and the authors' experience to date, including our preoperative screening, perioperative management, and detailed steps of the procedure.

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)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.634
Threshold uncertainty score0.728

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0040.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.001

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.

Opus teacher head0.019
GPT teacher head0.254
Teacher spread0.235 · 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 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

Citations17
Published2019
Admission routes2
Has abstractyes

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