Study of the correlation between vascular cognitive impairment and cholinergic pathway lesions in patients with acute ischemic stroke
Bibliographic record
Abstract
Objective To investigate the correlation between vascular cognitive impairment(VCI)and cholinergic pathway lesions in patients with acute ischemic stroke.Methods Eighty-seven patients with acute ischemic stroke(onset within 7 days)hospitalized in Neurologic Department of Tianjin Medical University General Hospital were recruited.The cognitive function was assessed with Mini-Mental State Examination(MMSE) and Montreal Cognitive Assessment(MoCA).Two trained raters evaluated magnetic resonance imaging(MRI)scans using MRI rating scales Cholinergic Pathways HyperIntensities Scale(CHIPS)and Fazekas)for white matter changes.Their value of application to VCI and correlations between the cognitive impairment and MRI rating scales were evaluated.Results Cognitive impairments were present in 79.3% of patients with acute ischemic stroke by the MoCA,but in only 26.4% of patients by the MMSE,the difference of frequency of cognitive impairment was statistically significant(P=0.000).CHIPS scores were negatively correlated with MMSE scores(r=-0.378,P=0.043)and MoCA scores(r=-0.504,P=0.005).There was no significant correlations between Fazekas scores and MMSE scores(r=-0.094,P=0.627),and MoCA scores(r=-0.410,P=0.056).CHIPS scores were negatively associated with visuospatial and executive function(r=-0.514,P=0.004),attention(r=-0.440,P=0.017) and Abstraction(r=-0.368,P=0.049).Fazekas scores were only negatively associated with attention(r=-0.404,P=0.030).Conclusion The present findings support the view that a significant correlation exists between VCI and white matter lesions within the cholinergic pathway in patients with acute ischemic stroke.The combined application of MoCA and CHIPS can be used as a good tool which can rapidly screen and evaluate VCI caused by white matter lesions.
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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 | 0.003 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".