Cognition, Statins, and Cholesterol in Elderly Ischemic Stroke Patients: A Neurologist’s Perspective
Bibliographic record
Abstract
Background and Objectives: The efficacy of hydroxy methyl glutaryl-coenzyme A reductase inhibitors (statins) in reducing the incidence of cardiovascular events pushed the target LDL-cholesterol (LDL-C) levels lower and lower in successive guidelines despite signals regarding potential cognitive side effects. We evaluated the relationship between cognitive impairment and LDL-C levels in elderly ischemic stroke patients. Materials and Methods: 29 ischemic stroke patients aged 65 and above with LDL-C levels ≤70 mg/dL, classified according to the TOAST criteria, underwent detailed neuropsychological testing comprising the MMSE test, Montreal Cognitive Assessment (MoCA) and Addenbrooke’s Cognitive Evaluation (ACE-III) test. Their performances were compared to those of 29 age-matched ischemic stroke patients with LDL-Cl levels >71 mg/dL. Results: The MMSE test failed to detect significant cognitive differences between the two groups. The MoCA and ACE-III tests detected impairments in visuo-spatial/executive function, attention, and recall/memory in patients with low LDL-C. A stepwise linear regression model of the ACE-III total scores revealed that LDL-cholesterol levels could contribute to 13.8% of the detected cognitive dysfunction, second in importance only to age, which contributed to 38.8% of the detected impairment. Conclusions: Physicians should be cautious when prescribing statins to elderly people. Hydrophilic ones may be preferred in cognitively impaired patients.
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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 | 0.001 |
| 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 | 0.002 | 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".