Rethinking the use of population descriptors in dermatology trials and beyond: disentangling race and ethnicity from skin color
Bibliographic record
Abstract
IMPORTANCE: Race and ethnicity as population descriptors in research and clinical practice have often been a subject of debate, drawing heightened scrutiny in recent years. Criticism focuses on their oversimplification and misapplication, which fail to capture the complexity of human health and genetic diversity. There is growing recognition that these categories, rooted in outdated social constructs, do not accurately reflect biological differences. OBSERVATIONS: Historically, race and ethnicity have been used as proxies for genetic variation and skin color, despite the understanding that these constructs are not biologically defined. The Skin of Color Society's second Meeting the Challenge Summit, attended by over 100 U.S. and international participants, highlighted several key themes: (1) the need for transparency in the rationale behind using population descriptors and decision-making processes; (2) recognizing the role of race and racism in dermatology; (3) exploring the intersection of dermatology, skin color, and cultural influences; (4) understanding the context of population descriptor usage; (5) developing improved, objective tools for classifying skin color; and (6) advancing research and creating guidelines. CONCLUSIONS AND RELEVANCE: There is an urgent need to reconsider the use of race and ethnicity as population descriptors in dermatology research. Current systems, which conflate social identity with biological markers, perpetuate health disparities and limit the accuracy of clinical data. Moving forward, more specific descriptors such as skin color, alongside socially determined factors, will be crucial in achieving meaningful diversity and inclusivity in clinical research.
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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 | 0.736 | 0.782 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.005 | 0.018 |
| Scholarly communication | 0.019 | 0.023 |
| Open science | 0.009 | 0.013 |
| Research integrity | 0.007 | 0.022 |
| Insufficient payload (model declined to judge) | 0.006 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".