Correlation between cognitive function and cerebral microbleeds in patients with small-artery occlusive stroke A prospective case series study
Bibliographic record
Abstract
Objective To investigate the correlation between cognitive function and cerebral microbleeds （CMBs） in patients with small artery occlusive stroke （SAO）. Methods The patients with SAO in Nanjing Stroke Registration Program were recruited from January 2011 to May 2011. The Montreal Cognitive Assessment （MoCA） was used to conduct the cognitive evaluation. At the same time, conventional MRI sequences and susceptibility-weighted imaging （SWI） were used to detect CMBs. Results A total of 70 patients with SAO were included in the study, 48 of them had abnormal MoCA scores （〈26 points） and 22 of them had normal MoCAscores （t〉26）. The age of patients （t = -2. 237, P =0. 023）, years of education （t =2. 297, P = 0. 029）, history of hypertension 0（2 = 2. 297, P = 0. 025 ）, severity of white matter hyperintensities （Z = -3.263, P =0. 001） and presence of CMBs （P =0. 001） were associated with the abnormal MoCA scores in patients with SAO. Logistic regression analysis showed that after adjusting for age, sex, white matter lesions, hypertension, diabetes and coronary heart disease, the presence of CMBs （odds ratio 5. 648,95% confidence interval 1. 105-28. 869; P = 0. 038） was still an independent risk factor for abnormal MoCA scores. The more serious of CMBs, the lower the MoCA scores （r = - 0. 532, P 〈 0. 001）. In patients with CMBs, the cognitive domain, such as the total MoCA score （t = 5. 180, P 〈 0. 001）, visuospatial/executive function （t = 3. 924, P 〈 0. 001 ） and attention （t = 4. 309, P 〈 0. 001 ） were impaired significantly. The CMBs at different parts resulted in cognitive impairment in the related fields. Conclusions The numbers of CMBs and their locations were closely associated with cognitive impairment in patients with SAO. Key words: Stroke; Brain ischemia; Cerebral hemorrhage; Cognition disorders; Magnetic resonance imaging
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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 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".