Preventive and therapeutic correction of post-stroke cognitive disorders in patients with atrial fibrillation
Bibliographic record
Abstract
Background. Cognitive decline (CD) during the recovery period of ischemic strokes in patients with atrial fibrillation (AF) is associated with left ventricular ejection fraction (LVEF) ≤ 48 %. Objective: to investigate cognitive changes during the recovery period of ischemic strokes in patients with AF and LVEF ≤ 48 % who took sacubitril/valsartan. Materials and methods. Patients with nonvalvular AF and LVEF ≤ 48 % who had an ischemic non-lacunar stroke within the last 6 months were examined. Thirty-nine people in the control group received protocol treatment, and 14 patients additionally took sacubitril/valsartan 100 mg twice daily for 6 months. Cognitive and echocardiographic parameters were assessed at the first examination and after 6 months. CD was diagnosed when the cognitive scores decreased by ≥ 1 point. Results. Both groups of patients at the time of inclusion did not differ significantly in cognitive functioning, socio-demographic, clinical, psycho-emotional characteristics, and echocardiographic parameters. Six-month administration of sacubitril/valsartan was associated with a significant reduction in the relative risk of CD on all used cognitive scales compared to the control group: by 3.18 times (95% confidence interval (CI) 1.42–7.16) on the Montreal Cognitive Assessment, by 2.79 times (95% CI 1.29–6.00) on the Clock Drawing Test and by 2.48 times (95% CI 1.19–5.14) on the Mini-Mental State Examination and the Frontal Assessment Battery. In addition, the sacubitril/valsartan intake was associated with a reliable increase in LVEF compared to baseline (40.3 (38.1–42.4) vs. 40.1 (39.4–42.5) %); in contrast, in the control group, no such pattern was observed. Conclusions. In patients with AF and LVEF ≤ 48 %, sacubitril/valsartan administration in the recovery period of ischemic non-lacunar stroke is associated with a significant improvement in cognitive functioning.
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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 | 0.001 |
| 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) | 0.001 | 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".