Efficacy and Safety of a New Resilient Hyaluronic Acid Filler in the Correction of Moderate-to-Severe Dynamic Perioral Rhytides: A 52-Week Prospective, Multicenter, Controlled, Randomized, Evaluator-Blinded Study
Bibliographic record
Abstract
BACKGROUND The perioral region is highly mobile and subject to multifactorial changes during aging. Resilient Hyaluronic Acid Redensity (RHAR), an RHA filler, was developed with the aim of optimizing outcomes in dynamic facial areas. OBJECTIVE This randomized, blinded, multicenter clinical study aimed to demonstrate superiority of RHAR over no-treatment control for correction of moderate-to-severe dynamic perioral rhytides. MATERIALS AND METHODS Blinded live evaluator assessments of efficacy included improvement in perioral rhytides severity using a proprietary scale (Perioral Rhytids Severity Rating Scale [PR-SRS]) and the Global Aesthetic Improvement Scale. Subjects self-assessed their results with FACE-Q, a validated patient-reported outcome measure, and satisfaction scales. Safety was monitored throughout the study based on common treatment responses (CTRs) and adverse events (AEs). RESULTS The primary efficacy end point was achieved, with the treatment group showing statistically significant superiority over the control group at Week 8 (80.7% vs 7.8% responder rate by PR-SRS, p < .0001). Most patients (66%) were still responders at Week 52 (study completion). Most AEs were CTRs after perioral injection of a dermal filler, and none was a clinically significant treatment-related AE. CONCLUSION Resilient Hyaluronic Acid Redensity is effective and safe for the correction of dynamic perioral rhytides in all Fitzpatrick phototypes, with marked durability.
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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 | 0.007 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".