Terminology, classification systems, and evaluation tools to describe skin of color in psoriasis and other dermatological conditions
Bibliographic record
Abstract
Health inequities regarding care for skin conditions impacting patients with skin of color (SoC) are of key importance, as skin color not only influences clinical care but also presents barriers to care access. Inadequate descriptions of skin color can prevent inclusive and equitable care for SoC patients with skin conditions such as psoriasis. This review examines the existing terminology, classification systems, and evaluation tools used to describe skin color and assesses the strengths and limitations of different approaches. Peer-reviewed studies were identified via targeted literature reviews. Race and ethnicity are often used as a substitute for skin color, but this approach lacks validity; there is a need for more objective and inclusive terminology and methods to precisely define skin color. Various classification systems are available to describe skin color, but the most common system, the Fitzpatrick scale, is limited in its utility. Colorimetry and spectrophotometry offer objective, reproducible measurements of skin color but are not widely used. This review underscores how inaccurate and incomplete descriptions of skin color can perpetuate health care disparities for patients with SoC, including those with psoriasis.
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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 | 0.027 | 0.072 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.018 | 0.012 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".