A Review of Aesthetic Considerations for Treating the Transgender Patient
Bibliographic record
Abstract
BACKGROUND: As gender diversity becomes increasingly embraced by society, and despite growing recognition of the unique needs of transgender patients, the literature remains devoid of guidelines for gender affirming facial feminization or masculinization techniques. OBJECTIVE: The authors seek to identify and discuss target feminine and masculine facial features, as well as an armamentarium of surgical and nonsurgical strategies to effectively address and achieve them in the transgender population. METHODS: A search of the National Library of Medicine database (PubMed) was undertaken to identify the existing literature on gender-affirming facial feminization and masculinization techniques. RESULTS: The importance of assessing proportional relationships between the bitemporal, bizygomatic, and bigonial distances is discussed; ideal masculine faces possess a rectangular face shape, with ratios for these 3 areas tending toward 1:1:1. Conversely, the ideal female face is heart shaped with projection at the zygoma and a tapered jawline. Strategic positioning of the cheek apex serves as an anchor in sculpting a distinctly masculine or feminine face. Other considerations include the enhancement of skin quality and implications of hormonal therapy. CONCLUSION: The aesthetic considerations provided in this study can serve as a valuable guidance for aesthetic physicians seeking to deliver optimal care for their transitioning patients.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".