Impact of cerebral microbleeds on cognitive functions and its risk factors in acute cerebral infarction patients
Bibliographic record
Abstract
Background Cerebral microbleeds (CMBs) are subclinical lesions of the brain parenchyma and an important marker for the clinical diagnosis of central nervous system vascular disease. However, the relationship between CMBs and cerebral infarction, cerebral hemorrhage, and cognitive impairment remains unclear.Methods In order to explore the cognitive function and risk factors of patients with acute cerebral infarction (ACI) complicated with cerebral microbleeds, 190 patients with ACI were collected. The patients were divided into groups with CMBs (n = 108) and groups without CMBs (n = 82) according to the presence or absence of CMBs. The general data, various examination indicators, Montreal Cognitive Assessment Scale (MoCA) scores of the two groups of patients were analyzed. Sixty healthy controls who underwent physical examination in our hospital during the same period were included as the healthy control group.Results ACI patients with CMBs had significantly higher rates of leukoaraiosis, hyperhomocysteinemia, hypercholesterolemia, and hypertension. Cognitive function was significantly lower in ACI patients with CMBs. Serum D-dimer, serum high-sensitivity C-reactive protein, serum neuron-specific enolase, and serum S100β of ACI patients with CMBs were all negatively correlated with their MoCA scores.Conclusion ACI patients with CMBs tended to have lower cognitive abilities than ACI patients without CMBs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".