Racial and Ethnic Differences in Self-Assessed Facial Aging in Women: Results From a Multinational Study
Bibliographic record
Abstract
BACKGROUND: Racial/ethnic variations in skin structure and function may contribute to differential manifestations of facial aging in various races/ethnicities. OBJECTIVE: To examine self-assessed differences in facial aging in women by race/ethnicity and Fitzpatrick skin phototypes. METHODS: Women aged 18 to 75 years in the United States, Canada, the United Kingdom, and Australia compared their features against photonumeric rating scales depicting degrees of severity for 10 facial aging characteristics. Impact of race/ethnicity (black, Hispanic, Asian, and Caucasian) and skin phototypes on severity was assessed. RESULTS: In total, 3,267 women completed the study. Black women reported the least severe facial aging; Caucasian women reported the most severe facial aging, with Asian and Hispanic women falling between these groups. Similarly, women with a skin phototype V/VI reported lesser aging severity than women with phototypes I through IV. More than 30% of black women did not report the presence of moderate/severe aging of facial areas until 60 to 79 years; most Hispanics and Asians did not report moderate/severe facial aging until 50 to 69 years and Caucasians, 40 to 59 years. CONCLUSION: In this diverse sample, black women reported less severe aging of facial features compared with Hispanic, Asian, and Caucasian women. These results were supported by Fitzpatrick skin phototype analyses.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".