A review of the topical management of acne and its associated sequelae in the Asia‐Pacific region with a spotlight on trifarotene
Bibliographic record
Abstract
Acne, a highly prevalent skin disease, can be particularly bothersome for patients of Asian background because of its impact on self-confidence and social interactions. In addition to active acne lesions, some patients may develop sequelae such as scarring, macular/postinflammatory hyperpigmentation, or erythema. The tendency of Asian skin to develop sequelae because of its increased susceptibility to irritation, cultural preferences for lighter skin phototypes, and differences in skincare regimens may all contribute to the increased burden of acne. Moreover, many Asia-Pacific countries do not have their own guidelines for acne management, and those that do often have no schedule in place for regular updates. In this article, we provide a critical review of the published guidance for the management of acne and its sequelae in the Asia-Pacific region, identifying gaps in current recommendations that could be addressed to enhance standards of acne care in Asia-Pacific countries. Along with highlighting the importance of a comprehensive skincare regimen to increase treatment efficacy and adherence, we discuss topical retinoids and retinoid combination options in the acne armamentarium that may be beneficial for sequelae prevention and management, such as adapalene 0.3% ± benzoyl peroxide 2.5%, tretinoin 0.05%, tazarotene 0.1%, and trifarotene 0.005%. In particular, trifarotene 0.005% has been observed to significantly reduce acne scar counts in a Phase 4 study. The recent data highlight the need to establish up-to-date guidance for acne and acne sequelae management in Asia-Pacific countries to provide optimal care to Asian patients.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| 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".