Dynamics of hyaluronic acid fillers formulated to maintain natural facial expression
Bibliographic record
Abstract
BACKGROUND: Subjects seeking facial rejuvenation want the results to appear natural. Currently, however, there is no consensus definition of, or assessment scale for, "naturalness." AIMS: This open-label pilot study explored evaluation techniques and criteria to assess naturalness of facial movement and expression following optimal bilateral correction of moderate-to-severe nasolabial folds and marionette lines with soft-tissue hyaluronic acid fillers formulated with XpresHAn Technology™. METHODS: Primary efficacy was investigator assessed naturalness of dynamic expressions using baseline and Day 42 posttreatment 2D video. Other evaluations included investigator assessed naturalness using static images, wrinkle severity, investigator and subject Global Aesthetic Improvement Scale assessments, and subject satisfaction. RESULTS: Defyne or both. Naturalness of dynamic expressions was at least maintained in all subjects. Naturalness of static expressions was not negatively affected in most subjects (96.7%). For dynamic expressions, 83.3% of subjects showed enhanced attractiveness, younger appearance and maintained naturalness. CONCLUSIONS: Overall, nasolabial folds and marionette lines improved significantly based on severity and Global Aesthetic Improvement Scale scores, with high subject satisfaction and favorable safety profile. Based on subject satisfaction and investigator assessments, using highly flexible hyaluronic acid dermal fillers did not compromise naturalness of lower facial expressions while achieving the desired improvements in attractiveness and youthfulness. The preliminary results obtained in this pilot study suggest that dynamic and static assessments of facial animation may aid the evaluation of natural outcomes in facial rejuvenation procedures.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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 | 0.000 |
| 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 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".