Factors Associated with Cognitive Outcomes After First-Ever Ischemic Stroke: The Impact of Small Vessel Disease Burden and Neurodegeneration
Bibliographic record
Abstract
BACKGROUND: Differences exist regarding post-stroke cognitive outcomes. OBJECTIVE: The aim of this study investigates the potential factors associated with post-stroke cognitive performance and trajectories. METHODS: We performed a prospective cohort study using serial monitoring of cognitive function over a 1-year period after a first-ever ischemic stroke. Small vessel disease (SVD) burden and hippocampal atrophy (HA) were evaluated using the modified cerebral small vessel disease scores (mCSVD) and medial temporal atrophy score (MTA) scores. A generalized estimating equation (GEE) model and a group-based trajectory model (GBTM) was used to analyze the potential factors associated with post-stroke cognitive outcomes. RESULTS: A total of 112 patients were enrolled. The GEE model showed that all patients, regardless of initial cognitive performance, had a tendency to show an increase in the Montreal Cognitive Assessment over time. The cognitive performance was better in male patients with higher education levels (p = 0.046 and p < 0.001, respectively), but tended to be worse in patients with higher SVD burden and HA. The GBTM model grouped patients into low, intermediate, and high performance (LP, IP, and HP) after stroke. A higher SVD burden, rather than HA and initial stroke severity and location, independently predicted a higher odds of poor post-stroke cognitive trajectory (being in the LP group) after stroke (adjusted odds ratio 2.74, 95%CI 1.09-6.86). CONCLUSION: In patients with first-ever mild stroke, cognitive improvement over time was evident. The detrimental impact of the SVD burden may outweigh the effect of HA or acute stroke insult on the post-stroke cognitive trajectory during the 1-year follow-up.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 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".