Fractional microneedle radiofrequency with the application of vitamin C, E, and ferulic acid serum for neck skin rejuvenation: a prospective, double-blinded, split-neck, placebo-controlled trial
Bibliographic record
Abstract
PURPOSE: To evaluate the efficacy of fractional microneedle radiofrequency (FMR) combined with topical antioxidant serum (vitamin C, E, and ferulic acid) compared to FMR alone for neck rejuvenation. MATERIALS AND METHODS: This prospective, randomized, double-blind, split-neck trial included 31 participants aged 30-65 years with visible signs of neck aging. Subjects underwent two FMR treatments at 4-week intervals. Immediately post-treatment, participants applied antioxidant serum to one randomly assigned side of the neck and placebo to the contralateral side daily. Efficacy was assessed by Fitzpatrick Wrinkle and Elastosis Scale, Global Esthetic Improvement Scale (GAIS), and biophysical skin parameters. Histological analyses evaluated elastin production and markers of senescence. RESULTS: < 0.001), and higher GAIS improvement (87.5% vs. 14.3%). Histologically, antioxidant-treated areas exhibited increased elastin and reduced cellular senescence markers (p16 and γ-H2A.X). CONCLUSIONS: Combining FMR with topical antioxidant serum substantially enhances neck skin rejuvenation, demonstrating superior clinical and histological outcomes. This approach effectively addresses neck aging, highlighting antioxidants as valuable adjunctive therapies.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".