Risk factors for cerebral microbleeds and its effect on cognitive function in patients with ischemic cerebrovascular disease
Bibliographic record
Abstract
Objectives To investigate the occurrence and risk factors of cerebral microbleeds on the cognitive function in patients with ischemic cerebrovascular disease and to investigate the effect of cerebral microbleeds on the cognitive function in patients with ischemic cerebrovascular disease.Methods A total of 160 patients with ischemic cerebrovascular disease were divided into cerebral microbleed group(n=30) and control group(n=130)according to MR T2～*-weighted gradient echo sequences.The demographic data of the patients,conventional risk factors,sites of microbleed,types of cerebral infarction,severity of white matter changes,and numbers of lacunar infarction were recorded in detail.The Montreal Cognitive Assessment was conducted in both groups.All the data were processed using SPSS 16.0 software.Results ①The mean age of the microbleed group was older than that in the non-microbleed group(75±9 vs.66±6,P= 0.000);the degree of white matter changes was higher(6.7±2.4 vs.3.5±1.8,P=0.003);and the numbers of lacunar infarction were larger(5.0±2.3 vs.2.1±1.3,P=0.000).②The logistic regression analysis showed that the high blood cholesterol,the degree of white matter change(WMC),and the numbers of lacunar infarction were the independent risk factors for CMB(OR=3.880,95% CI:0.086～0.994;OR= 11.735,95% CI:1.340～2.930;OR=10.160,95% CI:1.241～2.475).③There was no significant difference in the cognitive function scores between the two groups(P0.05).④There was significant difference in the executive ability between the two groups(P=0.010).Conclusions ①Cerebral microbleeds are closely associated with leukoaraiosis and lacunar infarction;②the patients with ischemic cerebrovascular disease accompanied by microbleeds has obvious executive dysfunction;③controlling the risk factors for microbleeds has a certain role for the control of the occurrence and development of cognitive impairment in patients with ischemic cerebrovascular disease.
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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 | 0.000 | 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 | 0.001 |
| 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".