Pediatric Patch Testing at Mayo Clinic between 2016 and 2020
Bibliographic record
Abstract
Abstract: Background: Allergic contact Dermatitis (ACD) is a common condition within the pediatric population. Patch testing is an important way to identify relevant allergens. Objective: To provide an update of the common contact allergens seen in children based on patch testing data at our institution from 2016 to 2020. Methods: We performed a retrospective analysis of patch test data from children aged 1–18 years from 2016 to 2020 at Mayo Clinic. Reaction rates were compared to the rates reported by the Pediatric Contact Dermatitis Registry (PCDR). Results: One hundred ninety-two children aged 1–18 were patch tested to various allergens. A total of 15,457 allergens were tested, with 291 positive tests. The top 5 allergens with highest positive reaction rates were hydroperoxides of linalool, hydroperoxides of limonene, methylisothiazolinone, nickel, and cobalt. Seven of the top 38 allergens with the highest reaction rates are not currently included in the Mayo Clinic Pediatric Patch Test Series, and 11 are not currently included in the Pediatric Baseline Series (as developed by the Pediatric Contact Dermatitis Workgroup). Conclusions: Patch testing is a useful tool to diagnose children with ACD. With new products and exposures, there is an opportunity to expand current pediatric patch testing series.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".