North American Contact Dermatitis Group Patch Test Results: 2019–2020
Bibliographic record
Abstract
Abstract: Background: Patch testing is an important diagnostic tool for assessment of allergic contact dermatitis (ACD). Objective: This study documents the North American Contact Dermatitis Group (NACDG) patch testing results from January 1, 2019, to December 31, 2020. Methods: At 13 centers in North America, patients were tested in a standardized manner with a screening series of 80 allergens, and, as indicated, supplemental allergens. Results: Overall, 4121 patients were tested; 2871 (69.7%) had at least 1 positive/allergic patch test reaction and 2095 patients (51.2%) had a primary diagnosis of ACD. The most commonly positive allergens were nickel (18.2%), methylisothiazolinone (MI) (13.8%), fragrance mix (FM) I (12.8%), hydroperoxides of linalool (HPL) (11.1%), and benzisothiazolinone (BIT) (10.4%). Compared with that of 2017–2018, prevalence of top 20 allergens statistically increased for FM I, HPL, BIT, propolis, and hydroperoxides of limonene (3.5%). For the first time, MI positivity did not increase between reporting periods. Approximately one-fifth of patients (20.3%) had ≥1 clinically relevant reaction(s) to allergens/substances not on the NACDG series. Conclusions: The epidemic of MI contact allergy in North America may have reached a plateau. Patch testing using a robust screening series, and supplemental allergens as indicated, is necessary for 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) | 0.017 | 0.004 |
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".