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.
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 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 | 0.014 | 0.032 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.022 | 0.044 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".