Correlation between cerebral microbleeds and cognitive impairment in patients with lacunar infarction and/or leukoaraiosis: a retrospective case series study
Bibliographic record
Abstract
Objective To detect the distribution of cerebral microbleeds (CMBs) in patients with lacunar infarction (LI) and/or leukoaraiosis (LA) and to analyze the correlation between the CMB related risk factors and cognitive impairment. Methods Thirty-eight patients with LI and/or LA were divided into either a CMB group or a non-CMB group according to the findings of susceptibility weighted imaging. The number of CMB lesions was recorded. Mini-mental state examination (MMSE) and Montreal cognitive assessment (MoCA) were used to conduct cognitive function tests, and the patients were also divided into a cognitive impairment group and a non-cognitive impairment group according to the MoCA scores. The demographic and clinical data in each group were compared. The independent risk factors for CMBs and cognitive impairment were identified. Results Thirteen patients had 58 CMBs in the CMB group. Their distributions were as follows: 36 CMBs in basal ganglia and thalamus, 14 in cortical and subcortical regions, 3 in brain stem, and 5 in cerebellum. There were 25 patients in the non-CBM group, 26 in the cognitive impairment group, and 12 in the non-cognitive impairment group. There were significant differences in age and the proportions of hypertension, taking antithrombotic drugs and the patients with LA between the CMB group and the non-CMB group (all P<0.05). Multivariable logistic regression analysis showed that only age was an independent risk factor for CMBs (odds ratio 1.103, 95% confidence interval 1.034-1.454; P=0.045). MMSE (26.92±2.87 vs. 29.00±1.44; t=2.452, P=0.027) and MoCA (21.62±3.36 vs. 25.04±2.59; t= -3.493, P=0.001) scores in the CMB group were significantly lower than those in the non-CMB group. There was only significant difference in the number of CMBs between the cognitive impairment group and the non-cognitive impairment group (2.08±3.64 vs. 0.33±0.78; t= -1.629, P=0.010). Multivariate logistic regression analysis showed that only the number of CMBs was an independent risk factor for cognitive impairment (odds ratio, 1.534, 95% confidence interval 1.100-2.576; P=0.046). Spearman rank correlation analysis showed that the number of CMBs was significantly negatively correlated with the MoCA language (r= -0.229, P=0.003) and the delayed recall (r=-0.332, P=0.042) scores. Conclusions In patients with LI and/or LA, CMBs were correlated with age. Their existence and number were associated with cognitive impairment. Key words: Cerebral Hemorrhage; Cognition Disorders; Stroke, Lacunar; Leukoaraiosis; Cerebral Small Vessel Diseases; Risk Factors
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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 | 0.000 | 0.000 |
| 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 | 0.000 |
| 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".