A 10-Year Evaluation of Skin Tone Diversity in the <i>Journal of Cutaneous Medicine and Surgery</i>
Bibliographic record
Abstract
To the Editor, Canada's population encompasses diverse bio-ancestral backgrounds across a wide range of skin tones.The representation of this diversity in the Canadian dermatology literature is unknown, although prior studies reported persistent disparities in the skin of color (SoC) representation in North American, European, and Asian dermatology journals.1 This study evaluated skin tone representation in the Journal of Cutaneous Medicine and Surgery (JCMS) over a 10 year period.Articles with photographs of skin surfaces were reviewed from JCMS between 2013 and 2022.Two independent reviewers assigned Fitzpatrick skin type (FPST) to 846 photographs with a third-party adjudicator for disagreements.Photographs were categorized into non-SoC (FPST I-III) and SoC (FPST IV-VI) phototypes.Geographical distributions were grouped into continents using the corresponding authors' country of affiliation.Images were also categorized into 4 etiologies: infectious, inflammatory, neoplastic, 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 | 0.002 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".