The Facial Platysma and Its Underappreciated Role in Lower Face Dynamics and Contour
Bibliographic record
Abstract
BACKGROUND: The platysma is a superficial muscle involved in important features of the aging neck. Vertical bands, horizontal lines, and loss of lower face contour are effectively treated with botulinum toxin A (BoNT-A). However, its pars facialis, mandibularis, and modiolaris have been underappreciated. OBJECTIVE: To demonstrate the role of BoNT-A treatment of the upper platysma and its impact on lower face dynamics and contour. MATERIAL AND METHODS: Retrospective analysis of cases treated by an injection pattern encompassing the facial platysma components, aiming to block the lower face as a whole complex. It consisted of 2 intramuscular injections into the mentalis muscle and 2 horizontal lines of BoNT-A injections superficially performed above and below the mandible (total dose, 16 onabotulinumtoxinA U/side). Photographs were taken at rest and during motion (frontal and oblique views), before and after treatment. RESULTS: A total of 161 patients have been treated in the last 2 years with the following results: frontal and lateral enhancement of lower facial contour, relaxation of high horizontal lines located just below the lateral mandibular border, and lower deep vertical smile lines present lateral to the oral commissures and melomental folds. CONCLUSION: The upper platysma muscle plays a relevant role in the functional anatomy of the lower face that can be modulated safely with neuromodulators.
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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 | 0.000 | 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) | 0.002 | 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".