Neuromodulators in Skin of Color: An International Review
Bibliographic record
Abstract
BACKGROUND: As the racial/ethnic diversity of the US population grows, it is imperative for dermatologists to recognize the nuances in the aesthetic treatment of diverse populations. OBJECTIVE: This comprehensive review explores the safety and efficacy of botulinum toxin A (BTX-A) in skin of color (SOC) populations and highlights variations in aging patterns, skin properties, and aesthetic concerns in SOC populations. MATERIALS AND METHODS: A review of PubMed/MEDLINE databases from 2004 to 2024 was performed using combinations of the terms botulinum toxin, SOC, Fitzpatrick, race/ethnicity, and Asian, Latin American, Caribbean, Middle Eastern, African, and Pacific countries. RESULTS: Twenty-three articles examining the use of BTX-A in SOC populations were identified. Twelve studies were from East Asia, 5 from the United States and/or Canada, 3 from South Asia/Southeast Asia, 2 from South America, and 1 from the Middle East. Available data suggest that BTX-A is efficacious and well tolerated in SOC populations. CONCLUSION: Increased SOC representation in clinical trials may guide the development of tailored treatment approaches to optimize aesthetic outcomes for patients with SOC. A comprehensive knowledge of the variations in aging patterns, skin properties, and aesthetic concerns across SOC populations is essential for providing culturally sensitive cosmetic dermatologic care for diverse populations.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| 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".