Objectively Measured Sleep Characteristics and Incidence of Ischemic Stroke: The Sleep Heart Health Study
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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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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it