Chronic Cortical Cerebral Microinfarcts Slow Down Cognitive Recovery After Acute Ischemic Stroke
Bibliographic record
Abstract
Background and Purpose- Cortical cerebral microinfarcts (CMIs) have been associated with vascular dementia and Alzheimer disease. The aim of the present study was to evaluate the role of cortical CMI detected on 3T magnetic resonance imaging, on the evolution of cognition during the year following an acute ischemic stroke. Methods- We conducted a prospective and monocentric study, including patients diagnosed for a supratentorial ischemic stroke with a National Institutes of Health Stroke Scale score ≥1, without prestroke dementia or neurological disability. Cortical CMIs were assessed on a brain 3T magnetic resonance imaging realized at baseline, as well as markers of small vessel disease, stroke characteristics, and hippocampal atrophy. Cognitive assessment was performed at 3 time points (baseline, 3 months, and 1 year) using the Montreal Cognitive Assessment, the Isaacs set test, and the Zazzo's cancellation task. Generalized linear mixed models were performed to evaluate the relationships between the number of cortical CMI and changes in cognitive scores over 1 year. Results- Among 199 patients (65±13 years old, 68% men), 88 (44%) had at least one cortical CMI. Hypertension was the main predictor of a higher cortical CMI load (B=0.58, P=0.005). The number of cortical CMI was associated with an increase time at the Zazzo's cancellation task over 1 year (B=3.84, P=0.01), regardless of the other magnetic resonance imaging markers, stroke severity, and demographic factors. Conclusions- Cortical CMIs are additional magnetic resonance imaging markers of poorer processing speed after ischemic stroke. These results indicate that a high load of cortical CMI in patients with stroke can be considered as a cerebral frailty condition which counteracts to the recovery process, suggesting a reduced brain plasticity among these patients.
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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 | 0.001 |
| 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".