Racial and Ethnic Differences in Self-Assessed Facial Aging in Women: Results From a Multinational Study
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| 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) | 0.000 | 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 it