Risk factors for cognitive impairment in patients with asymptomatic carotid stenosis
Bibliographic record
Abstract
Objective To investigate the risk factors for cognitive impairment in patients with asymptomatic carotid stenosis (ACS). Methods Patients with ACS were enrolled. The related clinical data were collected, including age, gender, blood pressure, blood lipid, glycosylated hemoglobin, homocysteine (Hcy), white matter lesion (WML) and the degree of carotid stenosis. Montreal cognitive assessment (MoCA) was used to evaluate cognitive function. The patients were divided into either a cognitive impairment group (<26) or non-cognitive impairment group (≥26). Multivariate logistic regression analysis was used to identify the risk factors for cognitive impairment in patients with ACS. Results A total of 123 patients with ACS were enrolled in the study, including 45 (36.6%) in the cognitive impairment group and 78 (63.4%) in the non-cognitive impairment group. There were significant differences in the degree of carotid stenosis, WML severity, years of education, age, and Hcy level between the 2 groups (all P<0.05). Multivariable logistic regression analysis showed that severe carotid artery stenosis (odds ratio [OR] 3.232, 95% confidence interval [CI] 1.134-9.208; P=0.028), severe WML (OR 8.930, 95% CI 2.683-31.688; P=0.015), and hyperhomocysteinemia (OR 2.671, 95% CI 1.877-3.609; P=0.037) were the independent risk factors for cognitive impairment in patients with ACS, while years of education were an independent protective factor of cognitive impairment in patients with ACS (OR 0.607, 95%CI 0.461-0.817; P=0.043). Conclusions Cognitive impairment may occur in patients with ACS. Years of education are an independent protective factor of cognitive impairment in patients with ACS, and severe carotid artery stenosis, severe WML, and hyperhomocysteinemia are its independent risk factors. Key words: Carotid Stenosis; Cognition Disorders; Neuropsychological Tests; Risk Factors
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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".