Characterizing the lateral slope of the aging female eyebrow
Bibliographic record
Abstract
I deal eyebrow aesthetics were first described in 1974 by Westmore (1) as a lateral arch with the apex terminating above the lateral limbus of the iris, and with medial and lateral ends of the eyebrow at the same height.Since this description, the ideal shape has been refined, and studies have shown that surgeons and patients both prefer an eyebrow shape with a lateral upward slope in which the arch apex is positioned just lateral to the lateral limbus (2-4).These aesthetics provide a framework for brow rejuvenation.The focus of brow rejuvenation procedures is on position, shape and symmetry of the eyebrows.Potential pitfalls and criticisms of surgical brow lift techniques include an 'unnatural' shape and appearance, excessive elevation of the medial eyebrow, a medial or flat apex shape and overelevation of the entire eyebrow without lowering the medial segment, yielding a surprised appearance (5-7).Because of these criticisms, surgeons performing brow rejuvenation procedures focus on reshaping the eyebrows more than repositioning.However, to accomplish this, we must fully understand the aging process at the eyebrow.Recently, Matros et al (8) studied eyebrow position in two groups of Caucasian women: 20 to 30 and 50 to 60 years of age.They found that the eyebrow overall was positioned higher in the older group and that the medial eyebrow was elevated more than the lateral eyebrow.This study helps to explain why brow lifting techniques that preferentially elevate the medial or mid portion of the eyebrow lead to an unnatural appearance.A similar study by Patil et al (9) also showed these results in a female Indian population, illustrating that this pattern transcends ethnicity.These two studies compared different points on the eyebrow between subjects, in isolation of one another and, in doing so, they do not fully capture the shape of one subject's eyebrow before comparison.To do this, we must know how one point relates to another within each eyebrow.To capture this, we can calculate the lateral slope of the eyebrow.In the present study, we set out to characterize the shape changes occurring in the aging female eyebrow, specifically the changes in slope with increasing age, by performing photographic measurements of the eyebrows of 100 women 20 to 80 years of age.We hypothesized a decrease in eyebrow slope with increasing age. MethodsResearch ethics board approval was obtained through Western University Health Sciences Research Ethics Board (London, Ontario, HSREB# 18550E).Standardized anteroposterior facial digital photographs were obtained, after verbal consent, from a random cohort of original artiCle ©2013 Canadian Society of Plastic Surgeons.All rights reserved tL deLyzer, A Yazdani.Characterizing the lateral slope of the aging female eyebrow.Can J Plast surg 2013;21(3):173-177.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 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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".