Association Between Enlarged Perivascular Spaces and Early Acute Ischemic Stroke with Cognitive Impairment: A Cross-Sectional Study
Bibliographic record
Abstract
Background: Enlarged perivascular spaces (EPVSs) are commonly detected via magnetic resonance imaging. It is unclear whether EPVSs are associated with cognitive impairment within one month after an acute ischemic stroke (AIS) (i.e., early AIS with cognitive impairment (EAIS-CI)). This study explored the severity and location of EPVSs and their association with EAIS-CI severity and provides clinicians with early warning indicators before the onset of typical clinical symptoms in the Chinese population. Methods: The clinical data of 208 patients (176 AIS patients and 32 controls) were prospectively analyzed using the Montreal Cognitive Assessment Beijing version (MoCA-BJ) score as the primary group criterion and the Mini-Mental State Examination (MMSE) score as a supplementary criterion. When EPVS I as the main EPVS type detected by imaging, the basal ganglia (BG) is the area most severely affected. Statistical analysis was conducted on the relevant clinical data. Results: AIS patients were grouped based on MoCA-BJ scores. Age (p < 0.01), education level (p = 0.02), EPVS I as the main EPVS type (p < 0.01), the number of right-sided BG-EPVSs (p = 0.04), white matter hyperintensities (WMHs) (Fazekas scores: p = 0.02), brain atrophy (global cortical atrophy scores: p < 0.01, Koedam posterior atrophy visual scale scores: p = 0.01, medial temporal lobe atrophy scores: p < 0.01) and AIS lesion volume (p = 0.01) were significantly greater in the EAIS-CI group than in the EAIS without cognitive impairment group. The cognitive domains of attention (p = 0.04) and orientation (p < 0.01) were more closely associated with EPVS I as the main EPVS type. However, multivariate regression analysis did not identify EPVS I as the main EPVS type as the main risk factor for EAIS-CI (p = 0.098). Grouping by MMSE scores revealed that EPVS I as the main EPVS type was linked to low education level (p < 0.01) and was significantly associated with EAIS in individuals with cognitive dementia (p < 0.01). Conclusions: As a result of multiple factors, EAIS-CI is significantly associated with a low education level, BG-EPVS, WMHs, and worsening brain atrophy severity. Imaging markers, such as the severity of BG-EPVS, can assist in the early diagnosis and assessment of EAIS-CI. Clinical Trial Registration: The study was registered with the China Clinical Trial Registry (https://www.chictr.org.cn/), registration number: ChiCTR2000038819.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".