Dermatology: how to manage psoriasis and recognize differences in pathophysiology and presentation in patients with skin of colour
Bibliographic record
Abstract
Psoriasis is a chronic inflammatory skin condition that affects diverse ethnic groups with a wide spectrum of skin colours. There are significant differences in how psoriasis presents and impacts the quality of life in non-White individuals. Genetic variations as well as cultural and socioeconomic factors all play a role in such differences and have important implications for the management of psoriasis in skin of colour. Despite these differences, the current psoriasis management is similar across different ethnic backgrounds and is mainly guided by factors such as disease severity, medical comorbidities and patient preferences. This is largely due to the lack of sufficient evidence for psoriasis treatment tailored for patients with skin of colour as most clinical trials are composed of mainly White individuals. Therefore, the focus of this article is to review the current evidence on how epidemiology, clinical presentation and genetic differences in patients with skin of colour with psoriasis may impact treatment strategies. Additionally, pharmacological therapies available to date in these diverse patient cohorts are summarized in this article. The limited data published on this topic reveal a significant need for more investigations with the ultimate goal of incorporating recommendations for patients with skin of colour into the current guidelines for psoriasis treatment. Moreover, awareness of differences in psoriasis presentation amongst individuals with skin of colour may support patients to seek medical care sooner, which could result in earlier diagnosis and lead to improved patient outcomes.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".