Self-reported sleep disturbances among people who have had a stroke: a cross-sectional analysis
Bibliographic record
Abstract
BACKGROUND: Sleep disturbances and their potential association with stroke remains understudied at a population level. We sought to determine the prevalence of sleep disturbances among people who have effects of stroke compared with the general population. METHODS: We used data from people aged 18 years or older who responded to the sleep and chronic disease modules of the 2017-2018 cycle of the Canadian Community Health Survey (CCHS). We measured sleep disturbances by self-reports of having trouble staying awake most or all of the time; either short (< 5 h) or long (> 9 h) nightly sleep duration; having trouble going to or staying asleep most or all of the time; and never, rarely or sometimes having refreshing sleep. We used log-binomial and multinomial regression to investigate prevalence of sleep disturbances among respondents who reported effects of stroke compared with others, adjusting for confounding factors. RESULTS: We included 46 404 CCHS respondents, 682 of whom reported effects of stroke. The prevalence of sleep disturbances for those with effects of stroke was higher than among others in the sample with regard to trouble staying awake (13.0% v. 6.1%; adjusted relative risk [RR] 2.16, 95% confidence interval [CI] 1.59-2.94), short or long duration sleep (28.9% v. 10.0%; adjusted RR 1.93, 95% CI 1.57-2.38), trouble going to or staying asleep, (28.1% v. 17.6%; adjusted RR 1.53, 95% CI 1.28-1.83) and lack of refreshing sleep (41.1% v. 37.1%; adjusted RR 1.30, 95% CI 1.14-1.49). The prevalence of at least 1 reported measure of sleep disturbance was 61.6% among those with effects of stroke, compared with 48.2% among others (adjusted RR 1.28, 95% CI 1.18-1.40). INTERPRETATION: Self-report of having effects of stroke was associated with increased prevalence of sleep disturbances compared with the general population. Sleep disturbances were reported by a high proportion of respondents with effects of stroke, indicating the importance of screening for related disorders.
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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) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| 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".