Changes in Product Use and Quality of Life after Patch Testing in Children with Allergic Contact Dermatitis
Bibliographic record
Abstract
BACKGROUND: Patch testing is the standard to diagnose allergic contact dermatitis (ACD). OBJECTIVE: This study assessed the value of patch testing for product changes and quality of life in children with ACD. METHODS: In this cross-sectional survey, we used a questionnaire to follow up with families of ACD patients about changes since patch testing and counseling preferences. Eligible participants were 18 years or younger during expanded series or personalized patch tests at the Washington University School of Medicine from 2007 to 2020. RESULTS: Of the 43 enrolled participants, most were positive for multiple allergens (63%) and changed personal products after patch testing (71%). Only 26% of the families consistently read product labels before patch testing, compared with 66% after. Patients saw a mean relative reduction of 49% in severity of rash (8.2-4.2 of 10), 46% in interference with activities (5.7-3.1), and 51% in self-consciousness (7.0-3.4) since patch testing. Families gave favorable feedback for counseling on products to avoid (9.4 of 10 average rating of usefulness), product recommendations (8.5 of 10), and chemical names (7.9 of 10). CONCLUSIONS: Patch testing can lead to meaningful improvements in quality of life for most children with ACD. Counseling related to positive patch test results should include discussion of specific products to use and avoid.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".