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17. SHARING THE MONTREAL GENDER AFFIRMING VAGINOPLASTY: A 25 YEAR AND 2500 CASES EXPERIENCE

2022· article· en· W4220707937 on OpenAlexaboutno aff
Alexis Laungani, P. Brassard, Maud Bélanger

Bibliographic record

VenuePlastic & Reconstructive Surgery Global Open · 2022
Typearticle
Languageen
FieldMedicine
TopicFemale Genital Mutilation/Cutting Issues
Canadian institutionsnot available
Fundersnot available
KeywordsVaginoplastyMedicineTransgenderSex reassignment surgery (male-to-female)Retrospective cohort studyGeneral surgeryTransgender PersonSurgeryFamily medicineVaginaPsychologyHuman immunodeficiency virus (HIV)

Abstract

fetched live from OpenAlex

PURPOSE: Gender affirmation procedures have been dramatically and steadily increasing in both academic and private practices throughout the country over the past few years. While this was a long-needed change for transgender and non-binary individuals, specific training for surgeons and residents has not necessarily followed the rapid uptake in cases performed. Genital surgeries can be therefore very challenging because of possible associated serious complications, less known anatomy and overall less in-training exposure. This study aims at describing the Montreal vaginoplasty technique, its refinements over the years, and to identify means of reduction of complications for the surgeons beginning in the field. METHODS: A retrospective case review was performed of all 2500 penile inversion vaginoplasties carried out by the senior author between 2000 and 2021. Changes in the technique over the years were identified by reviewing operative protocols and postoperative pictures. Most common complications were reviewed. RESULTS: Analysis of the technique allowed to describe 10 steps suitable for teaching, and to identify changes made over the years in each step, along with a decrease in overall complication rate. CONCLUSIONS: The Montreal vaginoplasty technique is described along with insights on its evolution in both achieving better cosmetic and functional outcomes, but also with strategies identified to mitigate the most encountered complications.

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)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.033
Threshold uncertainty score0.846

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.054
GPT teacher head0.302
Teacher spread0.249 · 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

Citations0
Published2022
Admission routes1
Has abstractyes

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Same venuePlastic & Reconstructive Surgery Global OpenSame topicFemale Genital Mutilation/Cutting IssuesFrench-language works237,207