Response of 21 Hyaluronic Acid Fillers to Recombinant Human Hyaluronidase
Bibliographic record
Abstract
Background: One benefit of hyaluronic acid fillers is the ability to dissolve them using hyaluronidase. With the increasing number of fillers entering the market, it is crucial to understand each of these fillers' responsiveness to hyaluronidase. Methods: Twenty-one hyaluronic acid fillers of 0.2 mL aliquots each were placed on slides. Twenty units of recombinant human hyaluronidase were injected into the aliquots every 30 minutes for a total of 120 units recombinant human hyaluronidase injected over 3 hours. With each injection, videos and photographs were taken from bird's eye and lateral views to measure aliquot height. Stirring videos were graded by three oculoplastic surgeons, and these grades were used to categorize each filler's responsiveness. Results: Restylane Lyft, Restylane-L/Eyelight, and Resilient Hyaluronic Acid (RHA) 1/Redensity were the least resistant. The moderately resistant group comprised of Restylane Silk, Juvéderm Volbella, Revanesse Versa/Lips, and Belotero Balance on the less resistant side to Juvéderm Vollure, RHA 2, Restylane Contour, Juvéderm Ultra, Restylane Refyne, Belotero Intense, Restylane Kysse, RHA 3, Juvéderm Ultra Plus, and Restylane Defyne on the more resistant side. The most resistant were RHA 4, Juvéderm Voluma, Belotero Volume, and Juvéderm Volux. The most resistant fillers required 120 units of hyaluronidase per 0.2 mL filler to dissolve. Conclusions: With the increasing popularity of fillers comes the increasing need to dissolve them for both ischemic and nonischemic complications. The majority of hyaluronic acid fillers available on the market are very resistant to hyaluronidase, which must be considered when determining the amount of hyaluronidase to dissolve a particular filler.
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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 | 0.001 |
| 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".