Adapalene 0.1%/benzoyl peroxide 2.5% gel reduces the risk of atrophic scar formation in moderate inflammatory acne: a split‐face randomized controlled trial
Bibliographic record
Abstract
BACKGROUND: The efficacy of current topical acne treatments in mitigating the potential for acne scarring is not known. OBJECTIVE: To evaluate the effect of adapalene 0.1%/benzoyl peroxide 2.5% (A/BPO) gel compared to vehicle in reducing the risk of acne scarring. METHODS: Multicentre, randomized, investigator-blinded, vehicle-controlled, split-face study conducted over 6 months. Subjects were adults with active moderate facial acne vulgaris and at least 10 atrophic acne scars at baseline. Efficacy evaluations included counts of atrophic acne scars and primary acne lesions as well as a Scar Global Assessment (SGA; 5-point scale). RESULTS: After 6 months treatment, scar counts remained stable with A/BPO while increasing by approximately 25% with vehicle (mean scar count 11.58 vs. 13.55, respectively, at Month 6; P = 0.036). The percentage of subjects with a SGA of 'almost clear' (hardly visible scars) increased from 9.7% to 45.2% with A/BPO, whereas it did not change with vehicle (P = 0.0032). Total acne lesion counts decreased by 65% with A/BPO and 36% with vehicle (mean lesion count 8.5 vs. 16.1, respectively, at Month 6; P < 0.001). LIMITATIONS: Relatively small study group (31 subjects). CONCLUSION: Topical long-term treatment with A/BPO is effective in reducing the risk of atrophic scars and improving the global severity of scarring.
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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 | 0.001 |
| 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 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".