Association of Self-Reported Sleep Metrics With Imaging Markers of Small Vessel Disease and Cognition in Patients With TIA or Mild Stroke
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Disturbed sleep is common after stroke, yet its relationship with cerebral small vessel disease (SVD) and cognitive performance in the stroke population, particularly patients with TIA/mild stroke who are on the milder end of the cerebrovascular spectrum, remains understudied. We aim to examine the associations of self-reported sleep metrics with neuroimaging markers of SVD and cognitive performance in patients with TIA/mild stroke from 2 prospective stroke cohorts. METHODS: We studied adult patients with TIA/mild stroke (NIH Stroke Scale [NIHSS] score <7) who were consecutively recruited from Mild Stroke Study 3 (MSS3, University of Edinburgh) and the stroke cohort (the University of Hong Kong, HKU) during 2018-2022. Both MSS3 (N = 211) and HKU (N = 211) cohorts assessed SVD burden visually on brain MRI, cognitive performance using Montreal Cognitive Assessment (MoCA), and sleep quality using a structured sleep questionnaire at baseline visit. The primary outcomes were SVD markers, and the secondary outcome was total MoCA score. The associations of sleep metrics with SVD markers and cognitive performance were assessed using regression models, adjusted for demographics, vascular risk factors, history of depression and stroke, and study sites. RESULTS: = 0.02. DISCUSSION: Disturbed sleep, including longer in-bed time and longer sleep duration, was cross-sectionally associated with greater SVD burden and worse cognitive performance in patients with TIA/mild stroke. Future longitudinal studies are warranted to validate our findings.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".