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.
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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.950 | 0.962 |
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".