A Unified Approach to Facial Contours and Volume Correction: The Role of the Cheek and the Chin
Bibliographic record
Abstract
Background: Facial proportions and contours influence perceptions of beauty and youthfulness. In particular, the shape and definition of the midface and lower face impact the overall appearance of the face. Methods: This review provides anatomical evidence to support a holistic approach to facial analysis and rejuvenation that starts with assessment and treatment of the midface and lower face to create an aesthetically desirable facial balance. Results: The cheek and chin can be considered "anchors" or starting points within full-face treatment because of the noticeable impact of their treatment on the definition and contour of the entire face. Age-related changes in the cheek and chin affect overall facial appearance and can produce unintended facial emotional attributes such as looking tired, angry, or sad. Patients seek facial aesthetic treatment typically for a global improvement such as revitalization or genderization of facial features. Best practices in aesthetics have evolved from treatment of individual areas to a holistic paradigm that uses multimodal therapy to improve overall facial emotional attributes. Hyaluronic acid fillers are useful for volume replacement and smoothing abrupt transitions that develop with age throughout the midface, chin, and jaw. A combination of hyaluronic acid filler for volume restoration and sodium deoxycholate and/or onabotulinumtoxinA for volume reduction where appropriate may optimize lower facial contour. Conclusions: This review highlights the importance of facial angles and contours as well as the significance of panfacial assessments and treatment, focusing on the relationships within areas of the face, specifically the midface and lower face, to optimize results.
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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 | 0.001 |
| 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".