Dermatology: how to manage acne in skin of colour
Bibliographic record
Abstract
Acne vulgaris is a prevalent dermatological condition worldwide but is especially challenging to treat in individuals with skin of colour (SOC). Corresponding to Fitzpatrick skin phototypes III-VI, people of African, Asian, Middle Eastern and Hispanic ethnicity are considered to have SOC. With the additional risk of postinflammatory hyperpigmentation (PIH) as a consequence of inflammatory acne or its respective treatment, managing acne in this population holds significant importance. PIH adversely impacts self-esteem and quality of life and, thus, is usually the patient's priority of treatment. Available acne treatments are similar for all skin types. However, some are more beneficial for individuals with SOC, in particular by targeting both active acne lesions and PIH. The acne treatment literature was searched for topical and systemic treatments that were specifically studied in the SOC population. These treatments included topical agents, such as retinoids and azelaic acid, in addition to topical antibiotics and benzoyl peroxide. Newer formulations and combined regimens reported effective in reducing lesions are less likely to induce PIH and may treat pre-existing PIH. Moisturiser use, titrating doses and patient education are strategies to minimize irritation and improve adherence. In addition, systemic therapies, including oral antibiotics, isotretinoin, oral contraceptives and spironolactone, are efficacious for refractory acne or more severe cases but specific studies in SOC are lacking. Chemical peels may improve acne and target PIH directly. Overall, based on limited evidence, topical and systemic therapies are well tolerated in the SOC population but efficacy should be balanced with the risk of adverse effects. This narrative review aims to highlight formulations and combination therapies that are effective and safe for treating acne and PIH in patients with SOC.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.021 | 0.018 |
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".