Effect of White Matter Changes on Cognitive Impairment in Patients With Lacunar Infarcts
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Cerebral white matter changes (WMC) and lacunar infarct are both believed to be consequence of small vessel disease. Whether the extent of WMC affect the type and degree of cognitive impairment in patients with lacunar infarct is not clear. The study was undertaken to determine if WMC influences cognition in patients with lacunar infarcts. METHODS: We recruited consecutive patients who were admitted to the acute stroke unit because of acute lacunar infarcts, mainly documented by diffusion-weighted magnetic resonance imaging. WMC were measured qualitatively and quantitatively. Patients were divided into quartiles according to the distribution of the volume of WMC. Cognition was assessed 12 weeks after stroke by psychometric tests (Chinese version of Mini-Mental State Examination [MMSE], Alzheimer's Disease Assessment Scale-cognition [ADAS-cog], Mattis Dementia Rating Scale-Initiation/ Perseveration subscale [MDRS I/P]) and was compared between patients with varying severity of WMC. Multivariate linear regression analysis was performed to find variables that influenced performance in the psychometric tests. RESULTS: Among the 94 included patients with acute lacunar infarcts, those patients (n=25) within the highest quartile of WMC were older, had more lacunar infarcts, more severe stroke, and lower prestroke cognitive function compared with those with less WMC. In addition, their performances in psychometric tests were significantly more impaired. Multivariate linear regression analysis revealed that WMC significantly influenced performance in MDRS I/P. WMC did not independently influence performance in MMSE and ADAS-cog. CONCLUSIONS: Extent of WMC appears to be associated with executive dysfunction in stroke patients with lacunar infarcts. Further large prospective studies with extensive scales of executive function testing are required to confirm this issue.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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".