17. SHARING THE MONTREAL GENDER AFFIRMING VAGINOPLASTY: A 25 YEAR AND 2500 CASES EXPERIENCE
Bibliographic record
Abstract
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.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".