Contact Dermatitis Associated with Food: Retrospective Cross-Sectional Analysis of North American Contact Dermatitis Group Data, 2001-2004
Bibliographic record
Abstract
BACKGROUND: Allergic and irritant contact dermatitis to food is likely underreported. OBJECTIVES: To characterize relevant allergens and irritants associated with food in patients referred to the North American Contact Dermatitis Group (NACDG) for patch testing. METHODS: Retrospective analysis of cross-sectional data from the NACDG from 2001 to 2004. RESULTS: Of 10,061 patch-tested patients, 109 (1.1%) had a total of 122 reactions associated with food. Approximately two-thirds of patients (66%) were female, and one-third (36%) were atopic. The hands were the most common sites of dermatitis (36.7%). There were 78 currently relevant (definite, probable, or possible) allergic reactions to NACDG standard series allergens with a food source; the most common allergen was nickel (48.7%), followed by Myroxilon pereirae (balsam of Peru) (20.6%) and propylene glycol (6.4%). Twenty allergic reactions to non-NACDG standard allergens and 24 relevant food irritants were also identified. Overall, 21% (25 of 122) of all reactions (irritant and allergic) were occupation related; the majority of these (17 of 25) were relevant irritant reactions. Cooks were the most commonly affected occupational group (40%). CONCLUSIONS: In this limited data set, nickel, Myroxilon pereirae, and propylene glycol were the most common allergens identified with a food source. Of food-related occupational disease, irritation was more common than allergy.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".