Predictors of dementia after first ischemic stroke
Bibliographic record
Abstract
ABSTRACT. Various mechanisms contribute to dementia after first ischemic stroke as lesions on strategic areas of cognition and stroke premorbidity. Objectives: Assessing clinical and neuroimaging predictors of dementia after first ischemic stroke and its relation to stroke location, subtypes and severity. Methods: Eighty first ischemic stroke patients were included. Forty patients with dementia after first stroke and forty patients without dementia according to DSM-IV diagnostic criteria of vascular dementia. All patients were subjected to general and neurological assessment, National Institute Health Stroke Scale (NIHSS) for stroke severity, Montreal Cognitive Assessment (MoCA) scale for cognition assessment, MRI brain and Trial of Org 10172 in acute stroke treatment (TOAST) classification for stroke subtypes. Results: Left hemispheric ischemic stroke, strategic infarctions, diabetes mellitus and stroke of anterior circulation were found to be independent risk factors for dementia after first ischemic stroke (OR=3.09, 95%CI 1.67-10.3, OR=2.33, 95%CI 1.87-8.77, OR=1.88, 95%CI 1.44-4.55, OR=1.86, 95%CI 1.45-6.54, respectively). Hypertension, dyslipidemia, smoking, ischemic heart disease, high NIHSS score and large vessel infarction were significantly higher among post stroke dementia patients. However, on binary logistic regression, they did not reach to be independent risk factors. Conclusion: Stroke location (left stroke, strategic infarction, anterior circulation stroke) and diabetes mellitus could be predictors of dementia after first ischemic stroke, but stroke severity, stroke subtypes, hypertension, dyslipidemia, smoking and ischemic heart could not.
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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) | 0.003 | 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".