Pediatric Contact Dermatitis Registry Inaugural Case Data
Bibliographic record
Abstract
BACKGROUND: Little is known about the epidemiology of allergic contact dermatitis (ACD) in US children. More widespread diagnostic confirmation through epicutaneous patch testing is needed. OBJECTIVE: The aim was to quantify patch test results from providers evaluating US children. METHODS: The study is a retrospective analysis of deidentified patch test results of children aged 18 years or younger, entered by participating providers in the Pediatric Contact Dermatitis Registry, during the first year of data collection (2015-2016). RESULTS: One thousand one hundred forty-two cases from 34 US states, entered by 84 providers, were analyzed. Sixty-five percent of cases had one or more positive patch test (PPT), with 48% of cases having 1 or more relevant positive patch test (RPPT). The most common PPT allergens were nickel (22%), fragrance mix I (11%), cobalt (9.1%), balsam of Peru (8.4%), neomycin (7.2%), propylene glycol (6.8%), cocamidopropyl betaine (6.4%), bacitracin (6.2%), formaldehyde (5.7%), and gold (5.7%). CONCLUSIONS: This US database provides multidisciplinary information on pediatric ACD, rates of PPT, and relevant RPPT reactions, validating the high rates of pediatric ACD previously reported in the literature. The registry database is the largest comprehensive collection of US-only pediatric patch test cases on which future research can be built. Continued collaboration between patients, health care providers, manufacturers, and policy makers is needed to decrease the most common allergens in pediatric consumer products.
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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.012 | 0.004 |
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".