Vascular risk factors aggravate cognitive impairment in first‐ever young ischaemic stroke patients
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Young ischaemic stroke patients often suffer from cognitive impairment after stroke. However, the risk factors of cognitive impairment are still unclear. This study examined the impact of vascular risk factors (VRFs) on cognitive impairment in first-ever young ischaemic stroke patients. METHODS: Subjects were divided into low (0-1 VRF, n = 27), medium (2-3 VRFs, n = 45) and high-risk (≥4 VRFs, n = 12) groups according to their number of VRFs. The following VRFs were collected: hypertension, diabetes mellitus, dyslipidaemia, atrial fibrillation, obesity, smoking, excess alcohol consumption, coronary heart disease and hyperhomocysteinaemia. A battery of cognitive assessments was executed 2 weeks after stroke. Differences of cognitive performances between groups were compared. The correlation between VRFs and cognitive function was investigated with an emphasis on discovering the main VRFs. RESULTS: Eighty-four patients were enrolled in this study eventually. Compared with the low-risk group, the high-risk group had significantly worse performance in most of the cognitive domains. VRFs had a correlation with general cognition, executive function, attention and verbal fluency. After adjusting the covariates, VRFs showed a linear correlation with global cognitive function (R = 0.640, P = 0.000), verbal fluency (R = 0.372, P = 0.000), delayed memory (R = 0.327, P = 0.002), visual attention (R = 0.290, P = 0.007) and executive function (R = 0.266, P = 0.015). Amongst all the VRFs, hypertension, hyperlipidaemia, smoking and hyperhomocysteinaemia were the main influencing VRFs. CONCLUSION: Vascular risk factors aggravate cognitive impairment after young ischaemic stroke. Effective management of VRFs in young adults is urgent and this may reduce the 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".