Atopic dermatitis in the pediatric population
Bibliographic record
Abstract
BACKGROUND: Little is known on the current global prevalence of atopic dermatitis (AD) in the pediatric population. OBJECTIVE: To estimate the real-world global prevalence of AD in the pediatric population and by disease severity. METHODS: This international, cross-sectional, web-based survey of children and adolescents (6 months to <18 years old) was conducted in the following 18 countries: North America (Canada, United States), Latin America (Argentina, Brazil, Columbia, Mexico), Europe (France, Germany, Italy, Spain, United Kingdom), Middle East and Eurasia (Israel, Saudi Arabia, Turkey, United Arab Emirates, Russia), and East Asia (Japan, Taiwan). Prevalence was determined using the following 2 definitions: (1) diagnosed as having AD according to the International Study of Asthma and Allergies in Childhood (ISAAC) criteria and self- or parent-report of ever being told by a physician that they or their child child had AD (eczema); and (2) reported AD based on the ISAAC criteria only. Severity was assessed using the Patient Global Assessment (PtGA) and Patient-Oriented Eczema Measure (POEM). RESULTS: Among 65,661 responders, the 12-month diagnosed AD prevalence (ISAAC plus self-reported diagnosis) ranged from 2.7% to 20.1% across countries; reported AD (ISAAC only) was 13.5% to 41.9%. Severe AD evaluated with both PtGA and POEM was generally less than 15%; more subjects rated AD as mild on PtGA than suggested by POEM. No trends in prevalence were observed based on age or sex; prevalence was generally lower in rural residential settings than urban or suburban. CONCLUSION: This global survey in 18 countries revealed that AD affects a substantial proportion of the pediatric population. Although prevalence and severity varied across age groups and countries, less than 15% had severe AD.
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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 | 0.001 |
| 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) | 0.003 | 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".