Allergic Contact Dermatitis and Patch Testing in Skin of Color Patients
Bibliographic record
Abstract
Abstract: Objective: Skin of color patients face important health issues relevant to dermatologists, such as allergic contact dermatitis; however, there is a lack of information surrounding common allergens causing contact dermatitis that disproportionately affect skin of color patients, as well as interpreting patch testing in this population. Methods: Covidence, Embase, MEDLINE, PubMed, Web of Science, and Google Scholar were searched to identify relevant articles studying allergic and irritant contact dermatitis in skin of color patients. Results: The most common positive reactions in African American patients included PPD, balsam of Peru, bacitracin, fragrance mix, and nickel. The most common positive reactions in Hispanic patients included Carba mix, nickel sulfate, and thiuram mix. The most common positive reactions in Asian patients included nickel sulfate, fragrance mix, and potassium dichromate. When interpreting patch test results in patients with higher Fitzpatrick skin types, positive patch tests presented with lichenification and hyperpigmentation, rather than erythema and vesicles. Furthermore, characteristic bright red or pink hues for positive results may appear violaceous or faint pink. Conclusions: Awareness of the common allergens associated with allergic contact dermatitis in patients of skin of color can help guide patch testing as an important diagnostic tool. Further research must be conducted regarding contact dermatitis in this patient population, especially given the relative lack of data surrounding Hispanic, Asian and Pacific Islander, and Native American patients.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".