Change in Cognition Following Ischaemic Stroke
Bibliographic record
Abstract
OBJECTIVE: Cognitive decline can occur following ischaemic stroke. How cognition changes over time and associations with cognitive change are poorly understood. This study aimed to explore these issues over 2 years following ischaemic stroke. METHODS: This analysis used data from the XILO-FIST study, a clinical trial of allopurinol versus placebo in people with ischaemic stroke according to Tissue-Based Definition. Participants underwent clinical assessment, brain MRI at baseline, and Montreal Cognitive Assessment (MoCA) at baseline, year 1 and year 2. We defined cognitive impairment as a MoCA score < 26 and cognitive change as a difference in MoCA score of 2 points or more at year 1 or year 2 after randomisation. Associations with cognitive impairment and cognitive change were assessed by univariable analysis and multiple logistic regression. RESULTS: Three hundred and sixty participants with complete MoCA data were included. Mean age was 65.4 (SD 8.36) years, and mean baseline MoCA score was 26.4 (SD 2.7). Seventy-seven participants had second-year cognitive improvement. Eighty-four had second-year cognitive decline. After adjustment for age and education year, second-year cognitive improvement was associated with smaller brain volume, lower albumin level, smoking and greater white-matter hyperintensity, and second-year cognitive decline was associated with peripheral arterial disease, higher cholesterol level, small-vessel stroke and greater white-matter hyperintensity. INTERPRETATION: Cognition is dynamic following stroke, with different patterns of change. Brain reserve and vascular risk factors relate to later post-stroke cognitive change. This complex nature of cognitive trajectory has implications for cognitive rehabilitation provision and cognitive impairment detection after stroke.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".