Trifarotene Reduces Risk for Atrophic Acne Scars: Results from A Phase 4 Controlled Study
Bibliographic record
Abstract
BACKGROUND: Atrophic acne scarring often accompanies acne vulgaris. The efficacy of topical retinoids for treatment of acne is well documented; however, evidence for use in atrophic acne scars is limited. METHODS: In this randomized, split-face, double-blind study, subjects (age: 17-34 years, N = 121) with moderate-to-severe facial acne, with acne scars present, were treated with either trifarotene 50 μg/g or vehicle once daily for 24 weeks. Efficacy was assessed by absolute and percent change from baseline in atrophic acne scar counts, Scar Global assessment (SGA), and IGA success rates as well as acne lesion counts. RESULTS: At week 24, a statistically significantly greater reduction in the mean absolute change from baseline in the total atrophic scar count was noted in the trifarotene- vs vehicle-treated area (- 5.9 vs - 2.7; p < 0.0001) with differences between sides noted as early as week 2 (- 1.5 vs - 0.7; p = 0.0072). The SGA success rate was higher in the trifarotene side at week 12 (14.9% vs 5.0%, P < 0.05) and improved through week 24 (31.3% vs 8.1%, P < 0.001). Similarly, at week 24, the IGA success rate was higher with trifarotene (63.6% vs 31.3%, P < 0.0001) along with reductions in total (70% vs 45%) and inflammatory (76% vs 48%) lesion counts. The incidence of treatment-emergent adverse events was 5.8% (trifarotene) and 2.5% (vehicle); most common (> 1%) was skin tightness (1.7% vs 0.8%), and all events were mild to moderate in severity. CONCLUSIONS: Trifarotene was effective and well tolerated in treating moderate-to-severe facial acne and reducing atrophic acne scars, with reduction of total atrophic scar count as early as week 2. TRIAL REGISTRATION: Clinicaltrials.gov NCT04856904.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".