Neuropsychological and neuroimaging assessments of early cognitive impairment in patients after mild ischemic stroke and transient ischemic attack
Bibliographic record
Abstract
This study aimed to identify markers of early cognitive impairment after acute mild ischemic cerebrovascular disease. To further explore the relationship between neuroimaging markers of vascular and neurodegenerative injuries and post-stroke cognitive impairment, 86 patients with transient ischemic attack/acute mild ischemic stroke were recruited. Demographic information, clinical data, stroke scale scores (Modified Rankin Scale, National Institutes of Health Stroke Scale), and neuroimaging parameters (medial temporal lobe atrophy, global cortical atrophy, white matter hyperintensities, location and number of acute infarcts) were collected. All participants underwent neuropsychological evaluation at the time of discharge. The neurocognitive assessment was conducted using the Montreal Cognitive Assessment-Basic and Trail-Making Test A. It was found that low Montreal Cognitive Assessment-Basic scores were associated with global cortical atrophy and lower education levels. The completion time on the Trail-Making Test A was significantly correlated with medial temporal lobe atrophy and less education. It is concluded that global cortical atrophy and lower education levels can be used as rapid indicators of early cognitive impairment in patients after a transient ischemic attack and acute mild ischemic stroke. Medial temporal lobe atrophy also appears to be associated with mental processing speed in patients after a transient ischemic attack and acute mild ischemic stroke.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 | 0.000 |
| 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".