Validity of a Self-Assessment Skin Tone Palette Compared to a Colorimeter for Characterizing Skin Color for Skin Cancer Research
Bibliographic record
Abstract
Our goal is to determine whether our objective 9-point Self-Assessment Skin Tone Palette (SASTP) is correlated with a colorimeter’s assessment of a melanin index, so that Hispanic and Black people can be included in skin cancer research where scales were developed for White populations. Subjects were asked to self-identify their skin tones using the SASTP. This study assessed the criterion validity of the SASTP by measuring a range of skin colors compared to a melanin index reported from a colorimeter for the upper-inner arm (non-sun-exposed skin color), and the outer forearm (sun-exposed). Among 188 non-artificial tanners, 50% were White, 30% were Hispanic or White-Hispanic, and 20% were other racial categories. Meanwhile, 70% were female (30% male) and 81% were age 18–29 (19% age 30+). The mean melanin of the upper-inner arm decreased with lighter skin color and stronger tendency to burn. The SASTP in comparison to melanin index values was correlated for both the upper-inner arm (r = 0.81, p < 0.001) and the outer forearm (r = 0.77, p < 0.001). The SASTP provides a 9-point scale that can be considered as an alternative, less expensive method that is comparable to the objective colorimeter melanin index, which may be useful in studies on skin cancer among White, non-White, and Hispanic peoples.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".