Prevalence of type 2 inflammatory diseases in pediatric patients with atopic dermatitis: Real-world evidence
Bibliographic record
Abstract
BACKGROUND: Patients with atopic dermatitis (AD) are considered at increased risk of developing other type 2 inflammatory diseases. However, real-world evidence based on large commercially insured pediatric populations in the United States is scarce. OBJECTIVE: To use a large claims database (IBM MarketScan 2013-2017) in the United States to assess prevalence and incidence of type 2 inflammatory diseases in pediatric patients with AD. METHODS: Pediatric patients with AD were matched 1:1 to patients without AD. Prevalence was assessed for conjunctivitis, rhinitis, urticaria, asthma, eosinophilic esophagitis, and chronic rhinosinusitis/nasal polyps at the 12 months' post-index date (the first AD diagnosis date for patients with AD; a randomly selected outpatient visit for control patients). The incidence of other type 2 inflammatory diseases post-index was assessed among patients 0-2 years of age. RESULTS: A total of 244,776 AD and matched non-AD patients were selected. The prevalence and incidence of type 2 inflammatory diseases were higher among patients with AD. Overall, the prevalence more than doubled for asthma, eosinophilic esophagitis, urticaria, and rhinitis, and increased with AD severity. LIMITATIONS: AD identification was based on billing diagnoses; the observation period was only 12 months; and the study was limited to commercially insured patients. CONCLUSION: The burden of type 2 inflammatory diseases in pediatric patients with AD is substantial, highlighting the need to optimize management of AD and its numerous associated morbidities.
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 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".