Association between Ankle Brachial Index, Brachial-Ankle Pulse Wave Velocity, and Mild Cognitive Impairment in Patients with Acute Lacunar Infarction
Bibliographic record
Abstract
BACKGROUND: The link between arterial stiffness and mild cognitive impairment (MCI) is receiving increasing attention, and the goal of this study was to explore the relationship among the ankle brachial index (ABI), brachial-ankle pulse wave velocity (Ba-PWV), and MCI in patients with acute lacunar infarction (ALI). METHODS: A total of 103 hospitalized patients with ALI were divided into a non-MCI group (n = 41) and an MCI group (n = 62) according to their Montreal Cognitive Assessment (MoCA) scores. A binary logistic regression model was used to assess the association among ABI, Ba-PWV, and MCI after adjusting for confounding factors. Spearman correlation was utilized to analyse the correlations between ABI, Ba-PWV, and MoCA total scores and sub-scores in ALI patients. RESULTS: Participants with cognitive impairment had significantly higher Ba-PWV and lower ABI than those with normal cognition. Correlation analysis suggested that Ba-PWV (r = -0.854, p < 0.05) and ABI (r = 0.734, p < 0.05) were correlated with MoCA total scores; of all MoCA sub-scores, visuospatial/executive function was the most strongly correlated with the vascular variables. In the binary logistic regression analysis, Ba-PWV (odds ratio [OR] = 4.507, 95% confidence interval [CI] = 2.152-9.441) and ABI (OR = 1.124, 95% CI = 1.015-1.254) were significantly associated with MCI, even after adjusting for lipoprotein (a) and systolic and diastolic blood pressure. CONCLUSION: The present study suggested that a higher Ba-PWV and a lower ABI were independent risk factors for MCI in patients with ALI.
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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 | 0.003 |
| 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 | 0.001 | 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".