Association of Childhood Atopic Dermatitis with Atopic and Nonatopic Multimorbidity
Bibliographic record
Abstract
BACKGROUND: Little is known about the impact of multimorbidity in childhood atopic dermatitis (AD). OBJECTIVE: We sought to determine the likelihood and predictors of chronic disease multimorbidity in childhood AD. METHODS: Data were examined for children (<18 years) in the 1996-2015 Medical Expenditure Panel Survey, an annual, representative sample of United States households. Multimorbidity was assessed using Charlson Comorbidity Index (CCI), Healthcare Utilization Project Chronic Comorbidity Indicator (HCUP-CCI) and frequency of atopic comorbidities. RESULTS: Young children with mild-moderate and severe AD, and adolescents with mild-moderate AD had higher CCI scores. Similarly, young children and adolescents with mild-moderate and severe AD had increased HCUP-CCI scores. Children with AD and atopic disease had higher CCI and HCUP-CCI scores than children with either alone. Young children and adolescents with mild-moderate and severe AD had more atopic comorbidities. CONCLUSIONS: Pediatric AD is associated with increased atopic and non-atopic multimorbidity. Comorbid atopic disease may identify a subset of children with AD who particularly benefit from enhanced screening and management of multimorbidity.
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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 | 0.003 |
| 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".