Contact Allergy in Canada versus United States: Analysis of the North American Contact Dermatitis Group Data 2005–2016
Bibliographic record
Abstract
OBJECTIVE: The aim of the study was to describe the differences in contact allergy between the United States (US) and Canada. METHODS: This is a retrospective cross-sectional analysis of the North American Contact Dermatitis Group data from 2005 to 2016. Frequencies of demographics, clinical characteristics, positive reactions, trends, and occupations were calculated. RESULTS: A total of 28,640 patients underwent patch testing. At least 1 positive patch test was observed in 18,599 patients (US, 11,641 [66.5%]; Canada, 6958 [62.5%]). When comparing the 2 groups, US positive reactions were more likely to occur in male patients (odds ratio [OR] = 1.40, 95% confidence interval [CI] = 1.31-1.49), older than 40 years (OR = 1.30, 95% CI = 1.22-1.38), Black (OR = 2.67, 95% CI = 2.24-3.19) or Hispanic race (OR = 3.53, 95% CI = 2.61-4.78), and/or patients with scattered generalized dermatitis (OR = 1.96, 95% CI = 1.80-2.13). They were less likely to occur in patients with eczema (OR = 0.61, 95% CI = 0.57-0.65) and Asian race (OR = 0.50, 95% CI = 0.44-0.56). Nickel (US, 16.0%; Canada, 22.4%) and methylisothiazolinone (US, 13.4%; Canada, 11.0%) were the top allergens. The third most frequent was neomycin (US, 11.7%) and fragrance mix I (Canada, 10.2%). CONCLUSIONS: National differences in allergen prevalence and trends exist in North America.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".