Effect of Carotid Artery Stenting on Cognitive Function in Patients with Internal Carotid Artery Stenosis and Cerebral Lacunar Infarction: A 3-Year Follow-Up Study in China
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Carotid artery stenting (CAS) is an important therapeutic strategy for patients with carotid artery stenosis. However, the potential influence of CAS on cognitive function in patients with carotid artery stenosis and cerebral lacunar infarction has not been determined. This study investigated changes in cognitive function associated with CAS and the factors related to these changes. METHODS: This prospective cohort study comprised 579 Chinese patients with cerebral lacunar infarction and carotid artery stenosis for whom CAS was indicated, and a matched control group of 552 healthy individuals. Cognitive function before CAS and at scheduled intervals from 6 months to 3 years was assessed with instruments that included the Mini-Mental State Examination (MMSE) and Montreal Cognitive Assessment (MoCA) scale. Potential factors that might affect cognitive function were analyzed via logistic regression. RESULTS: The MMSE and MoCA scores of the patients before CAS were significantly lower than that of the control subjects. These scores were significantly higher 6 months after CAS and sustained or increased throughout the 3-year follow-up. Also significantly improved after CAS from baseline were scores for an alternating trail test, cube copying, clock-drawing, attention, and delayed recall in an auditory-verbal learning test. Logistic regression analyses showed that age greater than 65 y, little education, diabetes, and hypertension were independent risk factors for deteriorated MoCA scores 3 years after CAS. CONCLUSION: CAS was associated with significantly improved cognitive function in cerebral lacunar infarction patients with severe stenosis.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".