Chonic Cortical Cerebral Microinfarcts Slow Down Cognitive Recovery After Acute Ischemic Stroke
Bibliographic record
Abstract
Background and Purpose. Cortical cerebral microinfarcts (CMI) have been associated with vascular dementia and Alzheimeru2019s disease. The aim of the present study was to evaluate the role of cortical CMI detected on 3T MRI, 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 supra-tentorial ischemic stroke with a National Institute of Health Stroke Score (NIHSS) u2265 1, without pre-stroke dementia or neurological disability. Cortical CMI were assessed on a brain 3T MRI realized at baseline, as well as markers of small vessel disease (SVD), stroke characteristics and hippocampal atrophy. Cognitive assessment was performed at three time-points (baseline, three months and one-year) using the Montreal Cognitive Assessment (MoCA), the Isaacs set test (IST), and the Zazzou2019s cancellation task (ZCT). Generalized linear mixed models were performed to evaluate the relationships between the number of cortical CMI and changes in cognitive scores over one-year.Results. Among 199 patients (65 u00b1 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 ZCT over one-year (B = 3.84, p = 0.01), regardless of the other MRI markers, stroke severity and demographic factors. Conclusion. Cortical CMI are additional MRI markers of poorer processing speed after ischemic stroke. These results indicate that a high load of cortical CMI in stroke patients 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 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.006 | 0.010 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.019 | 0.002 |
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; both teacher heads agree on what is shown here.
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".