Severity of Small Vessel Disease Biomarkers Reduces the Magnitude of Cognitive Recovery after Ischemic Stroke
Bibliographic record
Abstract
OBJECTIVE: The objective of this study was to evaluate the impact of radiological biomarkers suggestive of cerebral small vessel disease (SVD) on the evolution of cognitive performances after an ischemic stroke (IS). METHODS: We studied patients with a supratentorial IS recruited consecutively to a prospective monocentric longitudinal study. A cognitive assessment was performed at baseline, 3 months, and 1 year and was based on a Montreal Cognitive Assessment, an Isaacs set test of verbal fluency (IST), and a Zazzo's cancellation task (ZCT) for the evaluation of attentional functions and processing speed. The following cerebral SVD biomarkers were detected on a 3-T brain MRI performed at baseline: white matter hyperintensities (WMHs), deep and lobar microbleeds, enlarged perivascular spaces in basal ganglia and centrum semiovale, previous small deep infarcts, and cortical superficial siderosis (cSS). Generalized linear mixed models were used to evaluate the relationship between these biomarkers and changes in cognitive performances. RESULTS: A total of 199 patients (65 ± 13 years, 68% male) were analyzed. Overall, the cognitive performances improved, more significantly in the first 3 months. Severe WMH was identified in 34% of the patients, and focal cSS in 3.5%. Patients with severe WMH and focal cSS had overall worse cognitive performances. Those with severe WMH had less improvement over time for IST (β = -0.16, p = 0.02) and the number of errors to ZCT (β = 0.19, p = 0.02), while those with focal cSS had less improvement over time for ZCT completion time (β = 0.14, p = 0.01) and number of errors (β = 0.17, p = 0.008), regardless of IS volume and location, gray matter volume, demographic confounders, and clinical and cardiovascular risk factors. CONCLUSION: The severity of SVD biomarkers, encompassing WMH and cSS, seems to reduce the magnitude of cognitive recovery after an IS. The detection of such SVD biomarkers early after stroke might help to identify patients with a cognitive vulnerability and a higher risk of poststroke cognitive impairment.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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) | 0.001 | 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".