Volumizing with a 20-mg/mL Smooth, Highly Cohesive, Viscous Hyaluronic Acid Filler and Its Role in Facial Rejuvenation Therapy
Bibliographic record
Abstract
BACKGROUND: The new 20 mg/mL hyaluronic acid (HA) dermal filler is a smooth, highly cohesive, viscous formulation developed to restore volume in aesthetic facial rejuvenation. OBJECTIVE Evaluate clinical experience with 20 mg/mL HA dermal filler to date and comment on its current and potential uses within the facial rejuvenation treatment paradigm. METHODS AND MATERIALS: In this paper, the authors review the unique physical and chemical properties of 20 mg/mL HA dermal filler as well as clinical experience with the product to date. RESULTS: Overall, the 20 mg/mL smooth, cohesive, viscous HA filler was especially effective in restoring volume in the malar region and chin. Volume loss resolved significantly in patients in clinical trials(1) and treatment effects were observed to be maintained from six to 18 months.(2) Physicians reported the agent was highly effective as well as easy to inject, sculpt and mold. The treatment was generally well tolerated and no instances of product migration from the injection site have been reported. Patient satisfaction was high, with the vast majority of trial participants acknowledging they would return for additional treatment and recommend the treatment to friends.(1,2) CONCLUSION: Initial experience shows the 20 mg/mL smooth, cohesive, viscous HA filler to be a useful addition to the facial rejuvenation armamentarium when used both alone and in combination with BTX-A.
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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 | 0.001 | 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".