Sleep quality after stroke: A systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Sleep-related problems are debilitating and long-lasting conditions in individuals with stroke. We aimed to estimate the prevalence of poor sleep quality after stroke by conducting a systematic review and meta-analysis. METHODS: Five databases (PubMed, Embase, Web of Science, Scopus, and CINHAL) were searched for literature published before November 2022. Studies recruiting participants with stroke, using a validated scale to measure sleep quality and in English were included. We used the Agency for Healthcare Research and Quality Scale and Newcastle-Ottawa Scale to assess the quality of eligible studies. Pooled prevalence and subgroup analyses were performed to understand the variation in sleep quality among studies. We followed the PRISMA checklist to report the study. RESULTS: Thirteen studies were included for analysis (n = 3886). The pooled prevalence of poor sleep quality was 53% (95% CI 41-65%). Studies using PSQI with a cutoff point of 7 had a prevalence of 49% (95% CI 26-71%), whereas those with a cutoff point of 5 had a higher prevalence of 66% (95% CI 63-69%) (P = .13). Geographical location could explain the prevalence variation between studies. The majority of included studies had a medium level quality of evidence (10/13). CONCLUSION: Poor sleep quality appears to be common in patients with stroke. Considering its negative impact on health, effective measures should be taken to improve their quality of sleep. Longitudinal studies should be conducted to examine the contributing factors and investigate the mechanisms that lead to poor sleep quality.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.015 | 0.003 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.011 | 0.001 |
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; both teacher heads agree on what is shown here.
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".