A pilot study on the use of a non‐crosslinked hyaluronic acid with associated anti‐oxidant ingredients to improve the effectiveness of Nd:YAG laser toning in the treatment of melasma in six patients
Bibliographic record
Abstract
BACKGROUND: Melasma is a common skin disorder. Nd:YAG laser toning is a popular treatment for melanin clearance. Treatment efficacy is limited by factors such as presence of reactive oxygen species, DNA damage, TEWL, and skin barrier disruption. AIMS: The purpose of this pilot study is to explore the efficacy of a non-crosslinked hyaluronic acid with anti-oxidant ingredients in mitigating the above-stated factors in the treatment of melasma using Nd:YAG laser toning. METHOD: In this pilot retrospective study of six cases with melasma, Nd:YAG laser toning was performed for each case until improvement of melasma has plateaued (after 4-6 sessions) and at which point treatment was paused. After injecting non-crosslinked hyaluronic acid (9-12 mL) with anti-oxidant ingredients into the face with focus on the lesional skin, further sessions (4-6) of Nd:YAG laser toning was resumed. Before, interim (improvement has plateaued), and after (on completion of the remaining sessions of laser toning after hyaluronic acid injection) photos of the six cases were scored using modified MASI. ANOVA analysis was applied to the scores. RESULTS: All six melasma cases had further improvement in melasma clearance after hyaluronic acid injection beyond the point when improvement has plateaued. ANOVA analysis of before, interim, and after scores showed statistical significance in difference between the three groups. CONCLUSION: This pilot study of six cases suggests that the use of a non-crosslinked hyaluronic acid with anti-oxidants may improve the efficacy of Nd:YAG laser toning in the treatment of melasma.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".