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 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 | 0.001 | 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".