Bibliographic record
Abstract
PURPOSE OF REVIEW: There is a longstanding recognition of the detrimental effect of poorly controlled asthma on sleep, but recent years have seen a growing interest in how asthma and sleep may interact. This review examines the current evidence of relationships between asthma, sleep and sleep disorders. RECENT FINDINGS: Poor quality sleep and sleep disturbance is highly prevalent in asthmatic patients, and particularly in those with severe asthma. Impaired sleep quality correlates with worse asthma control and quality of life. Sleep disturbance in asthma may be related to due circadian variation in airway inflammation, but may also be related to specific sleep disorders. Obstructive sleep apnoea (OSA) appears to be significantly more common in asthmatic patients than nonasthmatic patients, and treatment of OSA with continuous positive airway pressure (CPAP) may lead to improved asthma-specific quality of life. Nocturnal CPAP may also be of benefit to asthmatic patients without OSA, potentially because of stretching of airway smooth muscle. Insomnia is also highly prevalent in severe asthma patients, and is associated with a history of poor asthma control and increased healthcare utilization. SUMMARY: Asthma, sleep and sleep disorders appear to have complex, but significant relationships. Prospective observational and controlled interventional studies are needed to quantify how addressing sleep difficulties may benefit asthma patients.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".