Long-Term North American Trend in Patch Test Reactions: A 32-Year Statistical Overview (1984–2016)
Bibliographic record
Abstract
BACKGROUND: Allergic contact dermatitis (ACD) remains a public health issue worldwide, despite regulations intended to minimize sensitization. With up-to-date knowledge about which chemicals continue to have high allergenicity, the government/industry can refocus their efforts to be most effective. OBJECTIVE: We reviewed updated data showing common allergens that elicit ACD to determine the progress in reducing sensitization to inform public health policy, government regulation, and industry standards. METHODS: We compiled data from the North American Contact Dermatitis Group showing patch test results from 1984 to 2016 for 153 compounds. Using these data, we analyzed the trends over time of positive test reactions to determine whether they are increasing or decreasing. RESULTS: Of the 47 compounds with sufficient data to analyze, 23 had a decreasing proportion of positive patch test results over the whole period. An additional 5 had a decreasing proportion over a shorter period. Finally, 4 had an increasing proportion over any period: compositae mix, methylchloroisothiazolinone/methylisothiazolinone, nickel sulfate, and thimerosal mix. CONCLUSIONS: The data strongly indicate decreasing and increasing frequency trends and challenge us to seek explanations, which are not yet clear. It is hoped that these data can be valuable in informing public health policy, government, and industry.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.013 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".