A Dynamic, High-Stretch Resilient Hyaluronic Acid Filler Improves Fine Lines of the Cheek and Patient-Reported Outcomes
Bibliographic record
Abstract
BACKGROUND: Radial cheek lines develop over time due to ultraviolet exposure, dynamic expression, loss of skin elasticity, and superficial fat due to aging. With the aging population and increasing social awareness, there is a surge in demand for treatment. OBJECTIVE: The purpose of this study was to evaluate efficacy of a novel, less-rigidly crosslinked, 15 mg/mL resilient, high-stretch hyaluronic acid filler for improvement of radial cheek lines. MATERIALS AND METHODS: Outcomes were assessed using photographs, the Allergan Fine Lines Scale, and Facial Appearance, Health Related Quality of Life and Adverse Effects - Questionnaire score Appraisal of Lines. Two treatments were performed, 4 to 6 weeks apart. Measurements were taken at baseline, at each follow-up treatment, and 4 weeks after the last treatment. RESULTS: Twenty subjects were enrolled. Allergan Fine Lines Scales showed significant improvement between baseline and 4 weeks after the first visit (3.4 ± 0.2, 3.2 ± 0.2, p = .049) and between baseline and 8 weeks after the first visit (3.4 ± 0.2 vs 2.35 ± 0.1, p < .01). Facial Appearance, Health Related Quality of Life and Adverse Effects - Questionnaire scores showed a significant improvement between the baseline and 4 weeks after the first visit (45.9 ± 3 vs 67.4 ± 4.2, p = .0004) and between baseline and 8 weeks after the first visit (45.9 ± 3 vs 62 ± 4.0, p = .0034). CONCLUSION: Treatment with a resilient hyaluronic acid seems to be effective in the appearance of fine lines of the cheek. This could potentially enhance patient self-assurance and improve quality of life by reducing the appearance of radial cheek lines and improve overall skin quality.
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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 | 0.000 |
| 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) | 0.002 | 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".