Frequency and Risk Factors of Vascular Cognitive Impairment Three Months after Ischemic Stroke in China: The Chongqing Stroke Study
Bibliographic record
Abstract
BACKGROUND: Frequency of poststroke cognitive impairment is high in western countries, and the risk factors of poststroke cognitive impairment have not been fully understood yet. We sought to examine the frequency and risk factors of cognitive impairment after ischemic stroke in a large stroke cohort of China. METHODS: A total of 434 consecutive patients with ischemic stroke were enrolled. The cognitive status before and 3 months after stroke was evaluated using the Informant Questionnaire on Cognitive Decline in the Elderly and the Mini-Mental State Examination, respectively. Poststroke cognitive impairment was defined as cognitive impairment with concomitant stroke, stroke-related cognitive impairment was defined as cognitive impairment developing after index stroke, and cognitive impairment after first-ever stroke was defined as cognitive impairment developing after first-ever stroke. Logistic regression analysis was used to find the risk factors of cognitive impairment after stroke. RESULTS: (1) Frequency of poststroke cognitive impairment was 37.1%, that of stroke-related cognitive impairment was 32.2%, and that of cognitive impairment after first-ever stroke was 29.6%. (2) The patients with cognitive impairment more often had older age, low educational level, atrial fibrillation, prior stroke, everyday drinking, left carotid territory infarction, multiple lesions, embolism, and dysphasia. (3) The factors associated with poststroke cognitive impairment in logistic regression analysis were age (OR 1.215, 95% CI 1.163-1.268), low educational level (OR 2.023, 95% CI 1.171-3.494), prior stroke (OR 5.130, 95% CI 2.875-9.157), everyday drinking (OR 2.013, 95% CI 1.123-3.607), dysphasia (OR 3.994, 95% CI 1.749-9.120), and left carotid territory infarction (OR 2.685, 95% CI 1.595-4.521). CONCLUSIONS: Cognitive impairment is common 3 months after ischemic stroke in Chinese people. Risk factors for poststroke cognitive impairment include age, low educational level, everyday drinking, prior stroke, dysphasia, and left carotid territory infarction.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 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".