Abstract 150: Cerebral Microbleeds and Cognition in Lacunar Stroke Patients: The Secondary Prevention of Small Subcortical Strokes Trial
Bibliographic record
Abstract
Background: Cerebral microbleeds (CMBs) have been associated with cognitive dysfunction. However, characterization of CMBs and cognitive dysfunction in a large population of stroke patients is lacking. We aimed to characterize the association of CMBs with cognitive function in lacunar stroke patients and the response to assigned treatments in the Secondary Prevention of Small Subcortical Strokes (SPS3) trial. Methods: SPS3 was a randomized trial comparing two systolic blood pressure targets and two antiplatelet regimens in 3020 dementia-free patients with symptomatic, MRI-confirmed lacunar stroke. Participants underwent detailed neuropsychological testing covering multiple domains, including short delay recall, long delay recall, discriminability, block design, symbol search, grooved pegboard, controlled oral word association and clock drawing, at baseline and then annually for 5 years. Participants with axial MRI T2*- GRE sequences allowing for CMB detection and Cognitive Abilities Screening Instrument (CASI) Z scores available at baseline were included in the current analysis. Results: CMBs were present in 30% of 1225 eligible patients (mean age 63 years, 35% women) with median follow up of 3 years. Patients with CMBs had greater proportion of mild cognitive impairment (MCI; 56% vs. 42%, p<0.0001) and lower cognitive scores at baseline (mean CASI Z score -0.9 vs. -0.5, p<0.0001), including significantly lower scores across most of the individual cognitive domains tested. During follow-up, in 578 participants without MCI, there was no difference in the rate of incident MCI in patients with CMBs (29%, 41/143) and those without CMBs (26%, 111/435; p=0.50). Trends in cognitive decline over time were not statistically significantly different between the two groups. There were no interactions between CMBs and treatment assignments for cognitive outcomes. Conclusions: In this largest reported cohort assessing the association between CMBs and post-stroke cognitive impairment, CMBs were associated with lower cognitive scores and greater rates of MCI at baseline. However, changes over time in cognition were similar in lacunar stroke patients with and without CMBs who receive adequate vascular risk management.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".