Safety and Effectiveness of Two High- <i>G</i> ′ Soft Tissue Fillers for Chin Augmentation: A Prospective, Randomized, Comparator-Controlled, Evaluator-Blinded Trial
Bibliographic record
Abstract
BACKGROUND: The chin is a crucial facial feature for overall attractiveness. Lower third imbalance can lead to signs of premature aging and loss of jawline contour. Effective chin augmentation with hyaluronic acid fillers has been demonstrated in the literature. Currently, however, no study has compared the safety and effectiveness of two high-G' fillers. OBJECTIVES: The objective of this study was to examine each filler's effectiveness in the correction of chin retrusion through a prospective, randomized, comparator-controlled, evaluator-blinded trial. METHODS: This study compared the safety and effectiveness of HASHA (Restylane Shaype) vs HAVLX (Juvederm Volux) for chin augmentation and correction of chin retrusion. A prospective, randomized, comparator-controlled, evaluator-blinded trial was conducted at a single research center. Forty participants aged 18 years or older with mild to severe chin retrusion were included and randomly allocated 1:1 to either HASHA (n = 20) or HAVLX (n = 20). The primary study endpoint was to examine differences in parameters associated with determining chin shape. Secondary endpoints included adverse events and patient satisfaction. RESULTS: Of the 40 participants enrolled in the trial, 37 (92.5%) were female and 3 (7.5%) were male. Although an independent-samples t-test revealed no statistically significant difference in total volumes of filler used with HAVLX (mean [standard deviation], 1.85 [0.69] mL) or HASHA (mean, 1.86 [0.89] mL, P = .953). When focused on the menton/pogonion injections, HAVLX required 15.27% more product than HASHA (mean, 1.48 mL vs 1.27 mL, P = .28). There was a statistically significant difference in efficiency score for correcting labiomental angle, with the mean efficiency score being 2.57 [1.67] for HASHA and 1.50 [1.11] for HAVLX (P = .02). CONCLUSIONS: With no statistically significant difference in overall volume utilized between HASHA and HAVLX, HASHA injections required 15.27% less volume in the menton/pogonion to achieve visual correction. Additionally, HASHA demonstrated a significantly higher efficiency score for correction of nasomental angle. Secondary endpoints were not significantly different and demonstrate that both products are safe and effective.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".