[Deeper skin tones rarely depicted in Dutch dermatology textbooks].
Bibliographic record
Abstract
OBJECTIVE: Within diverse populations such as in the Netherlands, medical education must prepare students to diagnose skin conditions on a broad range of skin tones. To develop the visual pattern recognition skills to do so, medical students need exposure to skin conditions on deeper skin tones. The purpose of this study is to assess the inclusion of images of brown skin in Dutch dermatology textbooks. DESIGN: Observational study. METHOD: Two large Dutch student textbook web shops were searched for dermatology textbooks, and all available general dermatology textbooks explicitly aimed at medical students were selected. All images of skin were examined and divided into the categories 'light skin', 'light to medium brown skin', 'medium to deep brown skin', 'deep to very deep brown skin', and 'indeterminate'. RESULTS: Five textbooks, with a total of 2060 images of skin, were examined. 87.6% of images showed light skin, 7.0% showed light to medium brown skin, 2.9% showed medium to deep brown skin, and 0.5% showed deep to very deep brown skin. 2.0% was categorized as 'indeterminate'. CONCLUSION: Dutch dermatology textbooks currently include only small percentages of images of brown skin. Unfamiliarity with disease presentation on deeper skin tones can lead to delayed diagnosis and worse outcomes in Black and Brown patients. Future textbooks should include images of different skin tones, including deeper ones, for every skin condition.
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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 | 0.000 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.015 | 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".