Analysis of Risk Factors for Cerebral Microbleeds and the Relationship between Cerebral Microbleeds and Cognitive Impairment
Bibliographic record
Abstract
(1) Background: Cerebral microbleeds (CMBs) are attracting increasing attention. Nevertheless, the risk factors for CMBs remain poorly identified, and the relationship between CMBs and cognitive impairment is still up for debate; (2) Objective: The present study analyzed the risk factors for CMBs and probed into the potential correlations between the presence, number, and location of CMBs and cognition; (3) Methods: This study enrolled 406 subjects who underwent both brain 3.0-T magnetic resonance imaging scans and cognitive testing. Spearman correlation was used to assess the relationship between the number of CMBs and cognition. Multiple linear regression was utilized to analyze the relationship between the regions of CMBs and each cognitive domain; (4) Results: Multivariate logistic regression analysis results showed that age (odds ratio (OR) = 1.045, 95% confidence interval (95%CI; 1.009, 1.082)), smoking (OR = 3.604, 95%CI (1.995, 6.509)), hypertension (OR = 3.607, 95%CI (2.204, 5.901)), total cholesterol (OR = 0.611, 95%CI (0.467, 0.799)), and Amyloid-β1-42 (Aβ1-42) (OR = 1.028, 95%CI (1.018, 1.037)) were the influencing factors of CMBs. Education years (OR = 0.959, 95%CI (0.930, 0.988)), white matter lesions (OR = 2.687, 95%CI (1.782, 4.051)), and CMBs (OR = 21.246, 95%CI (5.728, 21.576)) were the risk factors for cognitive impairment. Hypertension increased the probability of deep CMBs (OR = 12.54, 95%CI (2.21, 71.28)), while Aβ1-42 elevated the probability of lobar CMBs (OR = 1.02, 95%CI (1.00, 1.03)). There was a linear correlation between the number of CMBs and Montreal Cognitive Assessment scores (r = −0.756, p < 0.001). However, CMBs in each region were not related to specific cognitive domains (p > 0.05), except CMBs in the mixed group that were negatively correlated with attention (OR = −0.669, 95%CI (−0.034, −5.270)); (5) Conclusions: Taken together, serum Aβ1-42 levels are related to the presence of CMBs. Cognitive impairment is correlated with the number of CMBs rather than their region. These findings suggest that CMBs play a role in cognitive impairment and that CMBs mark the presence of diffuse vascular injury and neurodegenerative brain damage.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| 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".