Sleep Patterns and the Risk of Acute Stroke
Bibliographic record
Abstract
<h3>Background and Objectives:</h3> Symptoms of sleep disturbance are common, and may represent important modifiable risk factors for stroke. We evaluated the association between a spectrum of sleep disturbance symptoms and risk of acute stroke in an international setting. <h3>Methods:</h3> INTERSTROKE is an international case-control study of patients presenting with first acute stroke and controls matched by age (+/- 5 years) and sex. Sleep symptoms in the previous month were assessed via a questionnaire. Conditional logistic regression estimated the association between sleep disturbance symptoms and acute stroke, expressed as odds ratios and 95% confidence intervals. The primary model adjusted for age, occupation, marital status and modified-Rankin Scale at baseline, with subsequent models adjusting for potential mediators (behavioural/disease risk factors). <h3>Results:</h3> Overall, 4,496 matched participants were included, with 1,799 of participants having experienced an ischemic stroke and 439 an intracerebral haemorrhage. Short sleep (<5hrs: 3.15, 2.09-4.76), long sleep (>9hr: 2.67, 1.89-3.78), impaired quality (1.52, 1.32-1.75), difficulty getting to sleep (1.32, 1.13-1.55) or maintaining sleep (1.33, 1.15-1.53), unplanned napping (1.59, 1.31-1.92), prolonged napping (>1hr: 1.88, 1.49-2.38), snoring (1.91, 1.62-2.24), snorting (2.64, 2.17-3.20) and breathing cessation (2.87, 2.28-2.60) were all significantly associated with increased odds of acute stroke in the primary model. A derived Obstructive Sleep Apnoea (OSA) score of 2-3 (2.67, 2.25-3.15) and cumulative sleep symptoms (>5: 5.06, 3.67-6.97) were also associated with a significantly increased odds of acute stroke, with the latter showing a graded association. Following extensive adjustment, significance was maintained for the majority of symptoms (not difficulty getting to/maintaining sleep and unplanned napping), with similar findings for stroke subtypes. <h3>Discussion:</h3> We found that sleep disturbance symptoms were common, and associated with a graded increased risk of stroke. These symptoms may be a marker of increased individual risk, or represent independent risk factors. Future clinical trials are warranted to determine the efficacy of sleep interventions in stroke prevention.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".