Cognitive Impairment and Seizures in Patients with Lacunar Strokes
Bibliographic record
Abstract
BACKGROUND: Lacunar infarcts and white matter changes have been linked to cognitive impairment. Patients with lacunar strokes can also develop seizures, although the relationship between the two remains unclear. The present study investigates whether seizures in patients with lacunar infarcts are related to the strokes or to an underlying neurodegenerative disorder leading to cognitive impairment. METHODS: The demographic features, vascular risk factors and scores on the National Institutes of Health Stroke Scale (NIHSS) on admission for the stroke and on the modified Rankin scale on discharge, as well as on the Mini-Mental State Examination (MMSE), were determined in patients with a lacunar stroke. They were compared between 44 patients with and 248 without subsequent seizures. RESULTS: Patients with seizures had a lower main NIHSS score (p = 0.00133) and a more severe MMSE score (p < 0.001). They remained significantly more dependent (p = 0.019) after hospital discharge. Smoking, as a vascular risk factor, appeared to occur less frequently in seizure patients (p = 0.039). On logistic regression analysis, only NIHSS and MMSE scores remained independent variables. CONCLUSIONS: Seizure occurrence in patients with a lacunar infarct is not related to the severity of the stroke but rather to the degree of cognitive impairment. The present study suggests that the seizures are not due to lacunar infarcts but are more probably the expression of an underlying neurodegenerative process that is also responsible for the mental deterioration.
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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) | 0.000 | 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 | 0.000 | 0.001 |
| 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".