Representation of Skin of Color in Rheumatology Educational Resources
Bibliographic record
Abstract
OBJECTIVE: To investigate the availability of images representing Black, Indigenous, and people of color in rheumatology educational resources. METHODS: Color images were collected from 5 major educational resources and cataloged by the resources they came from, underlying rheumatic conditions, and skin type. Fitzpatrick skin type (FST) was used to categorize images into "light," "dark," or "indeterminate." The images were initially scored by a fellow in the Division of Rheumatology and subsequently validated by a faculty member from the Department of Dermatology. RESULTS: Of the thousands of images reviewed, 1604 images met study criteria. FST validation from the Department of Dermatology resulted in the recoding of 111 images. The final scoring revealed 86% of the images to be light skin, 9% of images to be dark skin, and 5% of images to be indeterminate. CONCLUSION: The paucity of dark skin images in rheumatology resources is incongruent with current diversity estimates in the US. Significant efforts should be made to incorporate images of Black, Indigenous, and people of color into educational resources.
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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 | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
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".