Risk Factors and Cognitive Relevance of Cortical Cerebral Microinfarcts in Patients With Ischemic Stroke or Transient Ischemic Attack
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: It was recently demonstrated that cerebral microinfarcts (CMIs) can be detected in vivo using 3.0 tesla (T) magnetic resonance imaging. We investigated the prevalence, risk factors, and the longitudinal cognitive consequence of cortical CMIs on 3.0T magnetic resonance imaging, in patients with ischemic stroke or transient ischemic attack. METHODS: A total of 231 patients undergoing 3.0T magnetic resonance imaging were included. Montreal Cognitive Assessment was used to evaluate global cognitive functions and cognitive domains (memory, language, and attention visuospatial and executive functions). Cognitive changes were represented by the difference in Montreal Cognitive Assessment score between baseline and 28-month after stroke/transient ischemic attack. The cross-sectional and longitudinal associations between cortical CMIs and cognitive functions were explored using ANCOVA and regression models. RESULTS: Cortical CMIs were observed in 34 patients (14.7%), including 13 patients with acute (hyperintense on diffusion-weighted imaging) and 21 with chronic CMIs (isointense on diffusion-weighted imaging). Atrial fibrillation was a risk factor for all cortical CMIs (odds ratio, 4.8; 95% confidence interval, 1.5-14.9; P=0.007). Confluent white matter hyperintensities was associated with chronic CMIs (odds ratio, 2.8; 95% confidence interval, 1.0-7.8; P=0.047). The presence of cortical CMIs at baseline was associated with worse visuospatial functions at baseline and decline over 28-month follow-up (β=0.5; 95% confidence interval, 0.1-1.0; P=0.008, adjusting for brain atrophy, white matter hyperintensities, lacunes, and microbleeds). CONCLUSIONS: Cortical CMIs are a common finding in patients with stroke/transient ischemic attack. Associations between CMI with atrial fibrillation and white matter hyperintensities suggest that these lesions have a heterogeneous cause, involving microembolism and cerebral small vessel disease. CMI seemed to preferentially impact visuospatial functions as assessed by a cognitive screening test.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".