Associations of sleep characteristics with atopic disease: a cross-sectional study among Chinese adolescents
Bibliographic record
Abstract
BACKGROUND: Adolescence, as a transition between childhood and adulthood, is a critical stage for the long-term control of atopic diseases. We aim to determine if sleep characteristics are involved in the increased risk of atopic disease among adolescents. METHODS: Adopting the stratified cluster random sampling method, this cross-sectional survey included 4932 participants aged 12-18 years. The Chinese version of adolescent sleep disturbance questionnaire and the adolescent sleep hygiene scale were used to collect information on sleep problems and sleep hygiene, respectively. Logistic regression models were implemented to examine the associations of sleep with atopic diseases. RESULTS: Sleep duration was not found to be related with allergic diseases. By contrast, sleep-disordered breathing was associated with an increased risk of asthma (adjusted OR = 1.79, 95% CI 1.25-2.55), allergic rhinitis (adjusted OR = 1.95, 95% CI 1.52-2.49), and eczema (adjusted OR = 1.63, 95% CI 1.23-2.16); poor sleep physiology was correspondent to increased odds of asthma (adjusted OR = 1.69, 95% CI 1.24-2.29), allergic rhinitis (adjusted OR = 1.40, 95% CI 1.13-1.73) and eczema (adjusted OR = 1.66, 95% CI 1.32-2.09); non-optimal sleep environment was associated with an increased prevalence of asthma (adjusted OR = 1.52, 95% CI 1.08-2.12), allergic rhinitis (adjusted OR = 1.32, 95% CI 1.04-1.69) and eczema (adjusted OR = 1.53, 95% CI 1.19-1.96). CONCLUSIONS: As sleep-disordered breathing, poor sleep physiology and non-optimal sleep environment were associated with a higher risk of allergic diseases, the results of this study provide a new concept for the adjuvant treatment of allergic diseases in adolescents. Management strategies of allergic diseases should take regular screening and targeted treatment of sleep issues into account.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".