Comparison of self‐reported signs of facial ageing among Caucasian women in Australia versus those in the <scp>USA</scp>, the <scp>UK</scp> and Canada
Bibliographic record
Abstract
BACKGROUND/OBJECTIVES: Australians are more exposed to higher solar UV radiation levels that accelerate signs of facial ageing than individuals who live in temperate northern countries. The severity and course of self-reported facial ageing among fair-skinned Australian women were compared with those living in Canada, the UK and the USA. METHODS: Women voluntarily recruited into a proprietary opt-in survey panel completed an internet-based questionnaire about their facial ageing. Participants aged 18-75 years compared their features against photonumeric rating scales depicting degrees of severity for forehead, crow's feet and glabellar lines, tear troughs, midface volume loss, nasolabial folds, oral commissures and perioral lines. Data from Caucasian and Asian women with Fitzpatrick skin types I-III were analysed by linear regression for the impact of country (Australia versus Canada, the UK and the USA) on ageing severity for each feature, after controlling for age and race. RESULTS: Among 1472 women, Australians reported higher rates of change and significantly more severe facial lines (P ≤ 0.040) and volume-related features like tear troughs and nasolabial folds (P ≤ 0.03) than women from the other countries. More Australians also reported moderate to severe ageing for all features one to two decades earlier than US women. CONCLUSIONS: Australian women reported more severe signs of facial ageing sooner than other women and volume-related changes up to 20 years earlier than those in the USA, which may suggest that environmental factors also impact volume-related ageing. These findings have implications for managing their facial aesthetic concerns.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".