Epidemiologic Analysis of Change in Eyelash Characteristics With Increasing Age in a Population of Healthy Women
Bibliographic record
Abstract
BACKGROUND: Observations that eyelashes become thinner, shorter, and lighter, as women age has not been previously quantified. OBJECTIVE: This study was conducted to investigate associations between eyelash characteristics and age. MATERIALS AND METHODS: The upper natural eyelashes of 179 subjects were photographed and analyzed (digital image analysis); length, thickness, and darkness (intensity: 0 = white and 255 = black) were calculated. Linear regression, including race as a potentially confounding factor, was used to assess the association between age and mean eyelash characteristics. RESULTS: Subjects' mean age was 40.3 (±10.3) years; 46.1% were white, 36.5% Asian, 9.0% Hispanic, 5.1% East Indian, and 3.4% black. Mean eyelash length ranged from 6.39 (±1.02) to 7.98 (±1.15) mm (subjects aged 50-65 years and 22-29 years, respectively). Mean thickness ranged from 1.17 (±0.42) to 1.62 (±0.56) mm (subjects aged 50-65 years and 20-29 years, respectively). Mean intensity ranged from 118.2 (±19.8) to 129.4 (±17.3) (subjects aged 30-39 years and 50-65 years, respectively). Adjusted for race, eyelash length, thickness, and darkness decreased significantly with increasing age (p < .000, p = .0090, and p < .05, respectively). CONCLUSION: Advancing age among an ethnically diverse population of healthy women is associated with significant decreases in eyelash length, thickness, and darkness.
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How this classification was reachedexpand
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".