Where’s the colour? Advocating for morphological and antioppressive fluencies in dermatology
Bibliographic record
Abstract
Dermatology research, medical education and clinical practice have historically prioritized white patients and undertaught the morphology, epidemiology and natural history of cutaneous disease in brown skin.1 Dermatology is also among the least racially diverse specialties,2 despite increasing racial diversity at population levels.2 Clinically, trainees and clinicians report lower confidence diagnosing and managing dermatological conditions in brown skin.1 These historical and contemporary factors likely underpin issues of inefficient healthcare stewardship,3 cultural safety,2 delayed diagnoses and disproportionate morbidities and mortalities1,3 for racialized dermatology patients. ‘Blacks’ and ‘Coloured’ are pejoratives deeply entrenched in racism, yet still featured in scholarship. The British Journal of Dermatology has committed to mitigating dermatological health disparities4 by publishing more diverse literature and featuring equity‐deserving community perspectives. However, in reconciling, we must establish consistent and respectful language when describing patients and darker skin tones, much like standardized lexica used to describe skin morphology. We otherwise risk maintaining racial health disparities and perpetuating distrust in health services3 and 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 | 0.022 | 0.046 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.023 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.007 | 0.017 |
| Insufficient payload (model declined to judge) | 0.008 | 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".