The effect and relative importance of sleep disorders for all-cause mortality in middle-aged and older asthmatics
Bibliographic record
Abstract
BACKGROUND: Previous studies observed that sleep disorders potentially increased the risk of asthma and asthmatic exacerbation. We aimed to examine whether excessive daytime sleepiness (EDS), probable insomnia, objective short sleep duration (OSSD), and obstructive sleep apnea (OSA) affect all-cause mortality (ACM) in individuals with or without asthma. METHODS: We extracted relevant data from the Sleep Heart Health Study established in 1995-1998 with an 11.4-year follow-up. Multivariate Cox regression analysis with a proportional hazards model was used to estimate the associations between ACM and four sleep disorders among asthmatic patients and individuals without asthma. Dose-response analysis and machine learning (random survival forest and CoxBoost) further evaluated the impact of sleep disorders on ACM in asthmatic patients. RESULTS: A total of 4538 individuals with 990 deaths were included in our study, including 357 asthmatic patients with 64 deaths. Three multivariate Cox regression analyses suggested that OSSD (adjusted HR = 2.67, 95% CI: 1.23-5.77) but not probable insomnia, EDS or OSA significantly increased the risk of ACM in asthmatic patients. Three dose-response analyses also indicated that the extension of objective sleep duration was associated with a reduction in ACM in asthmatic patients compared to very OSSD patients. Severe EDS potentially augmented the risk of ACM compared with asthmatics without EDS (adjusted HR = 3.08, 95% CI: 1.11-8.56). Machine learning demonstrated that OSSD of four sleep disorders had the largest relative importance for ACM in asthmatics, followed by EDS, OSA and probable insomnia. CONCLUSIONS: This study observed that OSSD and severe EDS were positively associated with an increase in ACM in asthmatic patients. Periodic screening and effective intervention of sleep disorders are necessary for the management of asthma.
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".