Improving the Quality of Atopic Dermatitis Epidemiologic Research in the United States: A Systematic Review of Disease Definitions and Related Content in National Surveys
Bibliographic record
Abstract
Epidemiologic studies of atopic dermatitis (AD) are critical to improve our understanding of the disease burden and to identify numerous atopic and nonatopic comorbidities. Despite their widespread use, the definitions and related contents of AD in population-based surveys have not been comprehensively evaluated. To characterize the state of population-based health-related surveys in the United States that include AD or eczema, we conducted a systematic review of Ovid MEDLINE to study AD in the United States from database inception through March 26, 2025. Of the 916 articles screened, 24 were included in the analysis, which included 11 independent surveys. Most surveys rely on self-reporting of physician diagnosis of AD or the International Study of Asthma and Allergies in Childhood criteria, including a modified version of the International Study of Asthma and Allergies in Childhood criteria, which has not been validated. National surveys yield the largest sample and are typically repeated annually; however, some national surveys have not included questions on AD in recent years. National surveys do not use standardized definitions to identify AD and do not adequately capture the key aspects of AD burden. Therefore, future population-based surveys should address these issues.
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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 | 0.050 | 0.165 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.011 | 0.011 |
| Bibliometrics | 0.014 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| 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".