Allergic Contact Dermatitis in Children With and Without Atopic Dermatitis
Bibliographic record
Abstract
BACKGROUND: Prevalence and causes of allergic contact dermatitis (ACD) in children vary with time and geographical area. OBJECTIVE: This study aimed to determine the relevant allergens causing ACD in children and the relation between ACD and atopic dermatitis (AD). METHODS: A cohort study on 349 children (0-15 years old) patch tested over a 7-year period was conducted. RESULTS: Patch test results were positive for at least 1 allergen in 69.3% of patients and were relevant in 69.8%. The highest sensitization rate (76.7%) was observed in children who are 0 to 5 years old (n = 86, 64% females), followed by the group of 6- to 10-year olds (70%, n = 157, 47.8% females), whereas 62.3% of 11- to 15-year-old children (n = 106, 59.4%) were sensitized. The most frequent allergens were nickel (16.3%), cobalt (6.9%), Kathon CG (5.4%), potassium dichromate (5.1%), fragrance mix (4.3%), and neomycin (4.3%). Body areas mostly affected were upper limbs and hands (31%). Approximately one third of children also had AD. Allergic contact dermatitis was more widespread in children with AD. Patch tests resulted positive in 55.3% (50% relevant) of AD compared with 76.9% (77.5% relevant) of the children without AD. Sensitizers were similar to children without AD. CONCLUSIONS: Very young children showed a high rate of relevant positive patch test reactions to common haptens. Allergic contact dermatitis may easily coexist with AD.
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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 | 0.002 |
| 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 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".