Characterization of Pediatric Patch Testing: A Retrospective Review, 2020–2023
Bibliographic record
Abstract
Abstract: Background: Recent evidence shows similar rates of allergic contact Dermatitis® (ACD) among children and adults despite children accounting for less than 10% of patch testing subjects. With a need for in-depth analyses of pediatric ACD, we herein characterize a pediatric cohort at a large North American patch testing center. Methods: A retrospective chart review was conducted for 135 patients ages 1–17 years who underwent patch testing from July 2020 from August 2023. Data were stratified by age 1–5, 6–11, and 12–17 years. Significance-Prevalence Index Numbers (SPIN) were calculated. Results: A total of 86% were sensitized, 40% had a relevant reaction, and positivity rates were equal between males and females. Top allergens by SPIN differed with age, but overall were linalool hydroperoxides (SPIN = 11.01), propylene glycol (10.30), limonene hydroperoxides (10.27), fragrance mix I (5.62), and lanolin (4.90). In total, 14% of the top allergens were not represented on the North American Contact Dermatitis® Group standard series. Of those tested to personal products, 45% had positive reactions and 72% of which were relevant. Conclusions: Emulsifiers and fragrances were the most relevant allergen categories, with the impact of emulsifiers not previously reported. ACD may affect males and females equally in this population. Supplemental allergens and personal products tested “as-is” contribute to conclusive pediatric patch testing.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".