Burden of Sleep and Fatigue in US Adults With Atopic Dermatitis
Bibliographic record
Abstract
BACKGROUND: Intense itch associated with atopic dermatitis (AD) can negatively impact sleep. However, the nature of sleep disturbance and fatigue in AD has not been fully elucidated. OBJECTIVES: The aim of this study was to determine the burden of sleep disturbance and fatigue in US adults with AD. METHODS: This study used a cross-sectional, questionnaire-based survey using a nationally representative sample of 5563 adults from the 2005 to 2006 National Health and Nutrition Examination Survey. Respondents were asked about history of AD, sleep disturbance, and fatigue-related instrumental activity of daily living (IADL) impairment. RESULTS: There was no significant association between having AD and having a diagnosed sleep disorder (10.44% vs 7.27%; odds ratio [OR] [95% confidence interval (CI)], 1.49 [0.84-2.64]; P = 0.23); however, respondents were more likely to report sleep disturbances to clinicians (33.38% vs 23.67%; OR [95% CI], 1.62 [1.10-2.38]; P = 0.04). In multivariate regression models controlling for sociodemographic and lifestyle factors, adults with AD had higher odds of sleep disturbances, including shorter sleep duration (adjusted OR [95% CI], 1.61 [1.16-2.25]), trouble falling asleep (adjusted OR [95% CI], 1.57 [1.10-2.24]), and early morning awakenings (adjusted OR [95% CI], 1.86 [1.24-22.78]). Those with AD also had significantly higher odds of feeling unrested and feeling too tired to perform IADLs. CONCLUSIONS: United States adults with AD have significantly impaired sleep and fatigue affecting IADLs, and sleep disturbances may be underdiagnosed in this population.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".