Predictors of Hospitalization, Length of Stay, and Costs of Care Among Adult and Pediatric Inpatients With Atopic Dermatitis in the United States
Bibliographic record
Abstract
INTRODUCTION: Little is known about the risk factors of hospitalization for atopic dermatitis (AD). OBJECTIVES: We sought to determine associations of hospitalization for AD in the United States. METHODS: Data were analyzed from the 2002 to 2012 National Inpatient Sample. Atopic dermatitis hospitalizations were compared with controls, which included all hospitalizations without any diagnosis of AD excluding normal pregnancy/delivery, yielding a representative cohort of US hospitalizations. RESULTS: Both adults and children, who were admitted for AD or eczema, were more likely to have nonwhite race/ethnicity, lowest-quartile annual household income, Medicaid or no insurance, and fewer chronic conditions. Increased cost of care and prolonged length of stay were also associated with nonwhite race/ethnicities, lowest-quartile annual household income, Medicaid or no insurance, and having a higher number of chronic conditions. CONCLUSIONS: There are significant racial/ethnic and socioeconomic differences between patients hospitalized with AD versus without it, suggesting that there may be racial/ethnic and/or health care disparities in 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 | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".