Characterizing the lateral slope of the aging female eyebrow
Bibliographic record
Abstract
BACKGROUND: Ideal eyebrow aesthetics give a framework for brow rejuvenation and surgical procedures do not always provide satisfying results. Previous studies have shown elevation of the medial brow with aging; however, they failed to characterize overall shape changes. OBJECTIVE: To characterize changes in eyebrow slope with increasing age to better direct brow rejuvenation. METHODS: From standardized anteroposterior facial photographs of 100 women 20 to 80 years of age, eyebrow height was measured at the medial limbus and arch apex from a mid-pupillary horizontal. The slope of the eyebrow was calculated. Using group analysis, mean height and slope were compared using the Mann-Whitney U test. Regression analysis was used to determine the relationship between slope and age. RESULTS: Mean slope significantly decreased from 20 to 29 years of age to 40 to 49 years of age (0.22 versus 0.12; P=0.03), and then increased between 40 and 49 years of age and ≥60 years of age (0.12 versus 0.21; P=0.05). Medial height did not change significantly, and arch apex significantly decreased between 20 and 29 years of age and 40 and 49 years of age. Regression analysis showed a quadratic relationship between age and slope, with the decrease in slope until the fifth decade of life being directly related to increasing age. After this, age was not a significant contributor to slope changes. CONCLUSIONS: With increasing age, the slope of the eyebrow decreases until the fifth decade dependent on age. After the fifth decade, age no longer plays a significant role. Therefore, choice of brow lift technique should be carefully selected.
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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 | 0.001 | 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) | 0.001 | 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 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".