Gender-Affirming Vaginoplasty: A Comparison of Algorithms, Surgical Techniques and Management Practices across 17 High-volume Centers in North America and Europe
Bibliographic record
Abstract
Penile inversion vaginoplasty is the most common gender-affirming genital surgery performed around the world. Although individual centers have published their experiences, expert consensus is generally lacking. Methods: Semistructured interviews were performed with 17 experienced gender surgeons representing a diverse mix of specialties, experience, and countries regarding their patient selection, preoperative management, vaginoplasty techniques, complication management, and postoperative protocols. Results: There is significant consistency in practices across some aspects of vaginoplasty. However, key areas of clinical heterogeneity are also present and include use of extragenital tissue for vaginal canal/apex creation, creation of the clitoral hood and inner labia minora, elevation of the neoclitoral neurovascular bundle, and perioperative hormone management. Pathway length of stay is highly variable (1-9 days). Lastly, some surgeons are moving toward continuation or partial reduction of estrogen in the perioperative period instead of cessation. Conclusions: With a broad study of surgeon practices, and encompassing most of the high-volume vaginoplasty centers in Europe and North America, we found key areas of practice variation that represent areas of priority for future research to address. Further multi-institutional and prospective studies that incorporate patient-reported outcomes are necessary to further our understanding of these procedures.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".