Current Concepts in the Use of Small-Particle Hyaluronic Acid
Bibliographic record
Abstract
BACKGROUND: Soft-tissue augmentation with hyaluronic acid (HA) fillers has become one of the most popular cosmetic procedures performed. HA fillers represent safe and commonly used fillers. Several different HA fillers are available. The differences lie in the manufacturing process, allowing for tailored uses. A small-particle HA with lidocaine (SP-HAL; Restylane Silk; Galderma, Uppsala, Sweden) was approved by the US Food and Drug Administration in June 2014 but has been available for many years in Canada as Restylane Fine Lines and in Europe as Restylane Vital. METHODS: Relevant articles were reviewed relating to the composition, effectiveness, and safety of SP-HAL. We also discuss the author's extensive clinical experience in the use of this product in Canada. RESULTS: SP-HAL has demonstrated proven benefits for lip fullness, augmentation, and treatment of perioral rhytides. Although off-label in the United States, SP-HAL is also well suited for the treatment of superficial fine lines, including periorbital, forehead, marionette, and smile lines. In addition, it has also been used in the tear trough region. A novel application for SP-HAL includes use as a skinbooster with intradermal micropuncture. In this technique, small aliquots of product are injected so as to gradually rejuvenate the skin in areas such as the face and hands. Side effects of SP-HAL were generally transient and mild. The most common side effects were swelling, tenderness, bruising, pain, and redness. CONCLUSION: SP-HAL is an effective and safe HA filler with varied clinical uses.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 0.006 |
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".