Allergic Contact Dermatitis in Skin of Color: A Retrospective Study from a Comprehensive Patch Testing Center
Bibliographic record
Abstract
Abstract: Background: There are few studies reporting patch test results in skin of color patients, especially for Hispanic, Asian, and Indigenous populations. Objective: To characterize patch testing results in patients with skin of color at our center. Methods: A retrospective study of patients demonstrating at least 1 allergenic reaction in comprehensive patch testing (+, ++, +++, or +/−) with final interpretation as allergic by a board-certified dermatologist specializing in contact dermatitis. Results were stratified by self-reported race and the most common reactions for each group were characterized and compared to those of White patients. Results: A total of 1389 patients were identified; 270 (19.4%) having skin of color (1119 White, 102 Asian, 115 Black or African American, 44 Hispanic, 9 Indigenous). Most common reactions among Asian patients were nickel, methylisothiazolinone (MI), and hydroperoxides of linalool. In Black patients, MI, nickel, and p-phenylenediamine were most common. In Hispanic patients, MI, nickel, and formaldehyde were most common. The positivity of acrylates ( P < 0.001) and propylene glycol ( P < 0.05) in Asians as well as dyes and rubber accelerators in Black patients ( P < 0.05) was significantly higher than in White patients. Conclusions: Nickel and MI are common allergens in all groups, with certain allergens being overly represented in some groups. Differing cultural practices may result in these variations, emphasizing the need to capture patch testing trends in these populations.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".