Assessment and Treatment Strategies for the Aesthetic Improvement of the Lower Face and Neck
Bibliographic record
Abstract
Annie Chiu,1 Vince Bertucci,2 Daniel Dal’Asta Coimbra,3 Dan Li4 1The Derm Institute, Redondo Beach, CA, USA; 2Private Practice, Woodbridge, Vaughan, ON, Canada; 3Department of Cosmetic Dermatology at Santa Casa de Misericórdia, Rio de Janeiro, RJ, Brazil; 4Department of Plastic and Reconstructive Surgery, Chinese People’s Liberation Army General Hospital, Beijing, People’s Republic of ChinaCorrespondence: Annie Chiu, The Derm Institute, 1636 Aviation Boulevard, #201, Redondo Beach, CA, 90278, USA, Tel +1 (310) 939-9800, Fax +1 (310) 939-9888, Email drchiu@thederminstitute.comBackground: Interest in aesthetic rejuvenation of the lower face and neck is growing, but published expert guidance is limited.Objective: Review aesthetic concerns of the lower face and neck and provide expert guidance on evaluation and treatment.Methods: Twelve international experts participated in an advisory board on lower face and neck aesthetic treatment. They completed a premeeting survey and met twice, reviewing responses and discussing patient evaluation and treatment strategies. They developed decision tree algorithms on patient assessment and treatment planning and sequencing, using clinical cases as a reference.Results: Treatment concerns include neck and lower face skin laxity, structural bone deficiency, insufficient or excess volume, submental fat, jowls, platysma bands, and masseter muscle prominence. Advisors agreed that the lower face and neck may be the most challenging areas to assess and treat; treatment goals include lower facial contour and overall facial harmony/balance. Advisors recommended first ruling out a surgical approach, then determining whether midface treatment is needed to support the lower face, and lastly evaluating the lower face for significant submental fat, excess or insufficient volume, and structural bone deficiency. To treat the lower face and neck, an anatomical layer approach, moving from deep to superficial layers, beginning with structural support, was recommended. Assessment and treatment decision trees were based on this approach.Conclusion: The lower face and neck are important but underrecognized areas of aesthetic concern. This article provides expert guidance and a suggested algorithm for assessment and treatment aimed at achieving satisfying and harmonious facial aesthetic results.Keywords: Chin, jaw, patient satisfaction, plastic surgery, treatment outcomes, algorithms, decision trees
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".