Bibliographic record
Abstract
Objective: Stroke is a common long-term condition with an increasing incidence as the population ages. Stroke symptoms often include cognitive impairment. Dyslipidemia is a well-known risk factor for stroke. The aim of the study was to investigate the cognitive impairment, lipid profile characteristics and patterns of statin therapy in patients with non-hemorrhagic stroke. Design and method: In 100 patients with acute ischemic stroke (42% male, 69.2 ± 8.9 (M ± SD) years, current smoking 30%, abdominal obesity 74%, arterial hypertension 97%, myocardial infarction 14%, previous non-hemorrhagic stroke 35%, diabetes mellitus 19%, brachiocephalic atherosclerosis 78%, atrial fibrillation 23%, chronic heart failure NYHA II/III 32/4%, chronic kidney disease 12%, estimated glomerular filtration rate 67.1 ± 17.1 ml/min/1.73 m2, alcohol abuse 12%, anemia 13%, previous statin therapy 9% the cognitive impairment, lipid profile characteristics and frequency of statin therapy were assessed. Cognitive functions were evaluated by using the Montreal Cognitive Assessment (MoCA) scale. Results: 96 (96%) patients had cognitive disfunction: 20.1 ± 4.3 scores by MoCA scale. 86 (86%) patients had dyslipidemia. The mean levels of lipids were 5.5 ± 1.3 mmol/l for total cholesterol (TC), 1.1 ± 0.3 mmol/l for HDL-C, 3.6 ± 0.9 mmol/l for LDL-C and 2.1 ± 0.6 mmol/l for triglycerides (TG). 17 (17%) patients were treated by statins during hospitalization. All of them took a low-dose statin. Conclusions: 96% of patients with non-hemorrhagic stroke have cognitive impairment according to MoCA scale, 86% of patients have dyslipidemia, only 9% and 17% of patients receive low-dose statin therapy ambulatory and at hospital respectively. There is a substantial gap between the guidelines and real practice of statin therapy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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".