32. Gender Surgery Training in Plastic & Reconstructive Surgery Programs: A Description of the Current Academic Landscape
Bibliographic record
Abstract
Background: The volume of gender affirmation surgery has increased steadily in recent years. Gender surgery as a subspecialty is an increasingly prevalent component of plastic and reconstructive surgery practice; however, the gender surgery education landscape has not been described in detail. The purpose of this study is to describe the current gender surgery training landscape and determine program-specific predictors of gender surgery training. Methods: Plastic and reconstructive training programs in the United States (US) and Canada were included in this retrospective study. Data collected from November 2021 to February 2022 included dedicated gender affirmation surgery rotations, presence of gender surgery fellowships, number of integrated residents, program-specific subspecialty rotation durations, and presence of a research year in the training program. Data were obtained from publicly available sources and program coordinators, residents, fellows, and faculty were contacted directly for additional data. Univariate and linear and logistic regression models were used to establish relationships between gender surgery rotation duration, presence of gender fellows, and program details. Results: Ninety-four plastic and reconstructive surgery residency programs in the United States and Canada were included in this study. Two (2.13%) residency programs had gender surgery rotations in (6 weeks and 12 weeks, each). The number of months residents rotated on gender surgery was significantly predicted by increased number of integrated residents (β = 0.074, p = 0.041), increased number of aesthetic surgery fellows (β = 0.281, p = 0.012), and more months on craniofacial/pediatrics (β = 0.024, p = 0.012). Six (6.38%) residency programs had gender surgery fellows. The presence of gender surgery fellows was significantly predicted by more integrated plastic surgery residents (β = 0.829, p = 0.049) and the presence of a research year in the plastic surgery training program (β = 2.351, p = 0.019). Conclusion: The volume of gender surgery is increasing in North America; however, formalized training for gender surgery in plastic surgery remains limited. The presence of gender surgery training is currently best predicted by factors associated with larger academic institutions, including increased number of integrated plastic surgery residents, research years, and fellows.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".