North American Contact Dermatitis Group Patch Test Results: 2021–2022
Bibliographic record
Abstract
Abstract: Background: Patch testing is an important diagnostic tool for the assessment of allergic contact dermatitis (ACD). Objective: This study documents the North American Contact Dermatitis Group (NACDG) patch testing results from 2021 to 2022. Methods: At 12 centers in North America, patients were tested in a standardized manner with a screening series of 80 allergens. Results: Overall, 3056 patients were tested; 2200 (72.0%) had at least 1 positive/allergic patch test reaction and 1412 patients (46.6%) had a primary diagnosis of ACD. The most commonly positive allergens were nickel sulfate hexahydrate 5% and 2.5% petrolatum (24.9% and 22.1%, respectively), methylisothiazolinone (MI) (11.5%), hydroperoxides of linalool (10.1%), cobalt chloride hexahydrate (9.2%), and methylchloroisothiazolinone/methylisothiazolinone (9.0%). Compared with 2019–2020, prevalence of the top 20 allergens statistically increased for nickel ( P < 0.001), cobalt ( P < 0.01), gold ( P < 0.001), hydroperoxides of limonene ( P < 0.001), oleamidopropyl dimethylamine ( P < 0.01), dimethylaminopropylamine ( P < 0.01), and ammonium persulfate ( P < 0.001). MI positivity continued to decrease from its peak in 2017–2018. More than one-fifth of patients (n = 640, 21.1%) had at least one clinically relevant reaction to an allergen/substance not present in the NACDG screening series. Conclusions: The epidemic of MI contact allergy in North America appears to continue its descent. Fragrance allergy is still very common, but the composition of fragrance allergy markers appears to be changing. Patch testing using a robust screening series, and supplemental allergens as indicated, is necessary for the comprehensive evaluation of ACD.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.018 | 0.005 |
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".