Periodic Limb Movements and White Matter Hyperintensities in First-Ever Minor Stroke or High-Risk Transient Ischemic Attack
Bibliographic record
Abstract
Study Objectives: Emerging evidence suggests that periodic limb movements (PLMs) may contribute to the development of cerebrovascular disease. White matter hyperintensities (WMHs), a widely accepted biomarker for cerebral small vessel disease, are associated with incident stroke and death. We evaluated the association between increased PLM indices and WMH burden in patients presenting with stroke or transient ischemic attack (TIA), while controlling for vascular risk factors and stroke severity. Methods: Thirty patients presenting within 2 weeks of a first-ever minor stroke or high-risk TIA were prospectively recruited. PLM severity was measured with polysomnography. WMH burden was quantified using the Age Related White Matter Changes (ARWMC) scale based on neuroimaging. Partial Spearman's rank-order correlations and multiple linear regression models tested the association between WMH burden and PLM severity. Results: Greater WMH burden was correlated with elevated PLM index and stroke volume. Partial Spearman's rank-order correlations demonstrated that the relationship between WMH burden and PLM index persisted despite controlling for vascular risk factors. Multivariate linear regression models revealed that PLM index was a significant predictor of an elevated ARWMC score while controlling for age, stroke volume, stroke severity, hypertension, and apnea-hypopnea index. Conclusion: The quantity of PLMs was associated with WMH burden in patients with first-ever minor stroke or TIA. PLMs may be a risk factor for or marker of WMH burden, even after considering vascular risk factors and stroke severity. These results invite further investigation of PLMs as a potentially useful target to reduce WMH and stroke burden.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.002 | 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".