An Objective, Quantitative, Dynamic Assessment of Hyaluronic Acid Fillers That Adapt to Facial Movement
Bibliographic record
Abstract
BACKGROUND: Objective dynamic assessments are central to the evaluation of facial rejuvenation treatments. This study used three-dimensional digital stereophotogrammetry to generate a quantitative dynamic assessment of facial strain and hyaluronic acid filler efficacy. METHODS: Thirty women (aged 41 to 65 years) with moderate to severe bilateral nasolabial folds and marionette lines received Restylane Refyne, Restylane Defyne, or both, and were compared to a "younger" untreated group (n = 20; aged 25 to 35 years). Three-dimensional videos were collected at baseline and at day 42. Dynamic strain was analyzed at the marionette lines and nasolabial folds as subjects went from a neutral position through a series of facial expressions. RESULTS: Baseline nasolabial folds and marionette lines showed statistically significant (p ≤ 0.05) differences in the level of stretch between younger and older untreated subjects, with higher stretch profiles observed in the older cohort. In the older cohort, filler treatment reduced peak strain (stretch) in the nasolabial folds and marionette lines compared with baseline across all tested expressions, resembling the youthful strain profile of the younger untreated control group. Treatment was well tolerated. CONCLUSIONS: Quantitative dynamic strain analysis is an innovative method for evaluating the dynamic face following dermal fillers and provides objective evidence that such treatment results in stretch levels resembling a younger phenotype in areas prone to the effects of facial aging. The strain reduction following filler treatment objectively conveys a dermal tightening effect, likely secondary to the volumization of treated areas. Additional studies will refine the technology and associated procedures to optimize quantitative dynamic strain analysis for treatment planning and patient outcome optimization. CLINICAL QUESTION/LEVEL OF EVIDENCE: Therapeutic, IV.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".