Evaluating the role of small particle hyaluronic acid fillers using micro-droplet technique in the face, neck and hands: a retrospective chart review
Bibliographic record
Abstract
BACKGROUND: Loss of the viscoelastic properties of the skin is a primary sign of aging and contributes to the appearance of wrinkles. Hyaluronic acid (HA) fillers are one of the most commonly used treatments for age-related soft-tissue reduction and volume loss. Evidence is also emerging that HA fillers rejuvenate the skin. METHODS: A retrospective chart review was completed on 20 subjects treated with small particle HA (SP-HA), to investigate its effects on skin properties. Subjects having received three treatments in the face, neck, and/or hands were considered in the analyses. Skin hydration, trans-epidermal water loss (TEWL), and pH were assessed at baseline (injection #1), Week 4 (injection #2), Week 8 (injection #3), and Week 12 (follow-up). RESULTS: Treatment with SP-HA significantly improved hydration levels in the face, neck, and hands. Significant results were seen in the face following the first three treatments, with subjects moving up to the next hydration level (ie, hydration went from dry to moisturized) and by the second treatment in the neck and hands. TEWL scores on the face and neck remained within healthy values throughout all visits. At baseline, TEWL scores on the hands were within critical condition and after three injections they recuperated to healthy values, while pH values remained within the normal range throughout treatment. CONCLUSION: A treatment regimen consisting of three SP-HA injections was safe and well tolerated. SP-HA use demonstrated a hydrating effect while positively impacting the skin's ability to retain moisture.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| 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".