Periocular Aging Across Populations and Esthetic Considerations: A Narrative Review
Bibliographic record
Abstract
As the face ages, the skin, fat, muscle, and fascia descend, and the underlying bone, cartilage, and teeth may lose mass. Oculofacial aging is a multifactorial process that is influenced by genetic, environmental, and lifestyle factors. This review summarizes the patterns of oculofacial aging that are observed across populations, including variations in periorbital hollowing, eyelid ptosis, and skin elasticity. Evidence indicates significant variability in aging patterns between sex- and race-based subgroups. Nonetheless, there remains a paucity of research on the progression of aging in some under-studied demographic groups. Signs of oculofacial aging often become apparent to patients well before these changes reach full maturity in later years, leading many to seek early esthetic interventions. Others may present with more advanced signs of aging, motivating a diverse range of therapeutic options. We discuss minimally invasive esthetic interventions to mitigate the signs of aging, which may include botulinum toxin injections, dermal fillers, applied energy-based treatments (e.g., lasers), and emerging techniques such as micro-focused ultrasound and platelet-rich plasma therapies. We review evidence on outcomes related to patient satisfaction and quality of life following esthetic interventions for oculofacial aging. Finally, we outline ethical considerations and challenges faced with the delivery of esthetic surgery, including treatment complications and the influence of social media. This review provides a comprehensive overview of oculofacial aging patterns, its management, and important considerations for the provision of esthetic oculofacial treatment.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".