Cerebral microbleeds and cognitive impairment in patients with chronic cerebral ischemia
Bibliographic record
Abstract
Objective To investigate the correlation between cerebral microbleeds (CMBs) and cognitive impairment in patients with chronic cerebral ischemia. Methods From January 2015 to January 2017, patients with chronic cerebral ischemia admitted to the Department of Neurology, the Second Hospital of Hebei Medical University were divided into CMBs positive group and CMBs negative group according to the findings of susceptibility weighted imaging. Mini-Mental State Examination (MMSE) and Montreal Cognitive Assessment (MoCA) were used to compare the cognitive function of both groups of patients and analyze the effects of different parts of CMBs on cognitive function. They were divided into cognitive impairment group and non-cognitive impairment group according to MoCA scores. Multivariate logistic regression analysis was used to determine the independent risk factors for cognitive impairment. Results A total of 96 patients were enrolled, aged 45-83 years, 32 were females (33.3%) and 64 were males (66.7%). There were 51 patients (53.1%) in the CMBs positive group, and 45 (46.9%) in the CMBs negative group, there were 33 patients (34.4%) in the cognitive impairment group and 63 (65.6%) in the non-cognitive impairment group. In the CMBs positive group, 10 patients had lobar CMBs, 29 had deep brain and infratentorial CMBs, and 12 had mixed CMBs. Multivariate logistic regression analysis showed that age (odds ratio [OR] 1.115, 95%confidence interval [CI] 1.030-1.207; P=0.007), the number of CMBs (OR 1.254, 95% CI 1.064-1.479; P=0.007), Fazekas scale score (OR 2.697, 95% CI 1.012-7.185; P=0.047), and multiple lacunar infarction (OR 7.103, 95% CI 1.248-40.424; P=0.027) were the independent risk factors for cognitive impairment. Compared with the CMBs negative group, the rates of cognitive impairment and dementia in the CMBs positive group were higher. The total MoCA score, visual space and execution function, attention, and delayed recall scores were significantly lower (all P 0.05). The visual space and execution function, attention, and delayed recall in the mixed CMBs group were significantly lower than those in the non-mixed CMBs group (all P<0.05). Conclusion CMBs (especially lobar CMBs) may cause a decline in overall cognitive function, even dementia in patients with chronic cerebral ischemia, and they are most closely related to visual space and executive function, attention, and delayed recall. Key words: Cerebral Hemorrhage; Brain Ischemia; Cognition Disorders; Dementia, Vascular; Magnetic Resonance Imaging; Neuropsychological Tests; 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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".