An Objective, Quantitative Assessment of Flexible Hyaluronic Acid Fillers in Lip and Perioral Enhancement
Bibliographic record
Abstract
BACKGROUND: HARK is an FDA-approved flexible filler designed for lips. OBJECTIVE: To quantitatively evaluate subject outcomes by measuring the change in lip texture, color (redness), lip fullness, and lip and perioral surface stretch (dynamic strain) after treatment. METHODS AND MATERIALS: In this 8-week open-label, Phase IV multicenter study, subjects were treated with HARK in the lips and HARR and/or HARD in perioral wrinkles and folds as add-on treatment. Assessments included 2D photographic analyses of lip texture and color, and 3D photographic assessments of lip enhancement and dynamic strain. RESULTS: HARK significantly improved lip texture (p ≤ .002), lip redness (p < .001), and added fullness to the lips (lip enhancement measurements; p < .001), at Week 8 after treatment. In addition, lower lip wrinkles were significantly reduced (p = .007) and there was a reduction in upper lip wrinkles (not statistically significant). Surface stretch (dynamic strain) in the lip and perioral region was significantly increased after treatment (p < .001). CONCLUSION: This analysis provides an objective measure of the beneficial effects of flexible hyaluronic acid fillers in lip augmentation and perioral enhancement and demonstrates a significantly improved lip texture, red color, and fullness. A significant increase in surface stretch (dynamic strain) is indicative of tissue expansion and improvement in lip smoothness.
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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 | 0.002 | 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 | 0.000 |
| 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".