Objectively Measured Sleep Characteristics and Incidence of Ischemic Stroke: The Sleep Heart Health Study
Bibliographic record
Abstract
OBJECTIVE: Sleep disorders are associated with the prevalence of stroke. However, there is limited evidence regarding the association between objectively measured sleep characteristics and ischemic stroke. METHODS: Ischemic stroke was assessed during the mean follow-up period of 11 years in the Sleep Heart Health Study. Sleep parameters such as wake after sleep onset (WASO) and sleep efficiency (SE) were objectively measured based on in-home polysomnography records. Multivariable Cox regression analysis was utilized to examine the relationship between objective sleep characteristics and ischemic stroke incidence. RESULTS: This study involved 4204 participants (1978 males and 2226 females, 63.8±11.1 years). The incidence of ischemic stroke increased in individuals with long WASO, poor SE, and short sleep duration. Multivariable Cox regression analysis showed that WASO within the fourth quartile (hazard ratio [HR] 3.771, 95% confidence interval [CI] 1.805-7.877, P<0.001), third quartile (HR 3.009, 95% CI 1.433-6.317, P=0.004), and second quartile (HR 3.108, 95% CI 1.470-6.568, P=0.003) had a higher incidence of ischemic stroke than WASO within the first quartile. Poor SE (<80.0%) was also found to be a predictor for ischemic stroke (HR 2.220, 95% CI 1.244-3.960, P=0.007). Additionally, a short sleep duration (<6 h) was associated with an increased risk of ischemic stroke (HR 1.725, 95% CI 1.026-2.899, P=0.040). CONCLUSION: Our results revealed a relationship between WASO, SE, and sleep duration and ischemic stroke. Therefore, these sleep characteristics may be adequate predictors for the incidence of ischemic stroke.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".