Allergy-related outcomes and sleep-related disorders in adults: a cross-sectional study based on NHANES 2005–2006
Bibliographic record
Abstract
BACKGROUND: Epidemiological evidence between the sleep disorders and allergy-related outcomes is limited. OBJECTIVES: The purpose of the present study was to estimate the relationship between sleep disorders and allergy-related outcomes in adults. METHODS: We built logistic regression models to examine the associations between sleep disorders and allergy-related outcomes in adult participants using the 2005-2006 NHANES database. Allergy-related outcomes included sIgE levels, asthma, hay fever, sneezing, wheezing, and eczema. Sleep disorders included sleep latency, sleep length, sleep problems, OSA symptoms, and daytime sleepiness. A t-test was used for between-group comparisons. RESULTS: Participants with OSA symptoms had 2.72 × higher odds of experiencing hay fever and 1.54 × higher odds of having eczema compared to Non-OSA symptoms participants. Participants with insufficient sleep (≤ 6 h/night) had 1.27 × higher odds of developing allergic sensitisation compared to participants with adequate sleep (7-8 h/night). Sneezing was positively associated with sleep problems (OR: 1.706; 95% CI 1.386, 2.099), OSA symptoms (OR: 1.297; 95% CI 1.049, 1.605), and daytime sleepiness (OR: 1.569; 95% CI 1.205, 2.04). CONCLUSION: Our findings suggest a positive association between allergy-related outcomes and sleep disorders. In particular, OSA symptoms, daytime sleepiness, and sleep problems are strongly associated with allergic conditions.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 |
| 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".