A randomized, controlled comparative study of the wrinkle reduction benefits of a cosmetic niacinamide/peptide/retinyl propionate product regimen vs. a prescription 0·02% tretinoin product regimen
Bibliographic record
Abstract
BACKGROUND: Tretinoin is considered the benchmark prescription topical therapy for improving fine facial wrinkles, but skin tolerance issues can affect patient compliance. In contrast, cosmetic antiwrinkle products are well tolerated but are generally presumed to be less efficacious than tretinoin. OBJECTIVES: To compare the efficacy of a cosmetic moisturizer regimen vs. a prescription regimen with 0.02% tretinoin for improving the appearance of facial wrinkles. METHODS: An 8-week, randomized, parallel-group study was conducted in 196 women with moderate to moderately severe periorbital wrinkles. Following 2 weeks washout, subjects on the cosmetic regimen (n = 99) used a sun protection factor (SPF) 30 moisturizing lotion containing 5% niacinamide, peptides and antioxidants, a moisturizing cream containing niacinamide and peptides, and a targeted wrinkle product containing niacinamide, peptides and 0.3% retinyl propionate. Subjects on the prescription regimen (n = 97) used 0.02% tretinoin plus moisturizing SPF 30 sunscreen. Subject cohorts (n = 25) continued treatment for an additional 16 weeks. Changes in facial wrinkling were assessed by both expert grading and image analysis of digital images of subjects' faces and by self-assessment questionnaire. Product tolerance was assessed via clinical erythema and dryness grading, subject self-assessment, and determinations of skin barrier integrity (transepidermal water loss) and stratum corneum protein changes. RESULTS: The cosmetic regimen significantly improved wrinkle appearance after 8 weeks relative to tretinoin, with comparable benefits after 24 weeks. The cosmetic regimen was significantly better tolerated than tretinoin through 8 weeks by all measures. CONCLUSIONS: An appropriately designed cosmetic regimen can improve facial wrinkle appearance comparably with the benchmark prescription treatment, with improved tolerability.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".