QUALITY OF LIFE AND COGNITIVE IMPAIRMENT IN PATIENTS WITH CHRONIC HEART FAILURE
Bibliographic record
Abstract
Abstract. Вackground. Chronic heart failure (CHF) has a high prevalence in the Russian population. It is one of the main causes of reduced quality of life in cardiology patients due to the gradual progression of the disease and the development of complications, as well as the development of cognitive impairment. Early recognition of these disorders will optimize the management of patients with CHF. Purpose of the study. To evaluate the quality of life and cognitive functions in patients with different severity of chronic heart failure. Characterization of patients and methods of the study. 105 patients with confirmed diagnosis of CHF with different functional classes according to NYHA were included in the open cross-sectional non-randomized cohort study. Cognitive screening was performed using the Montreal Cognitive Assessment (MoCA), and quality of life was assessed by the Quality of Life Questionnaire (EQ-5D). Statistical Analysis Methods. Excel data analysis package and SPSS 23.0 data analysis package were used. Results and Conclusion. A pilot study was conducted to detect mild or severe cognitive impairment using the Montreal Cognitive Scale and quality of life using the EQ-5D questionnaire in patients with different functional classes of CHF. A significant correlation was found between cognitive function and left ventricular ejection fraction. Quality of life did not depend on the severity of CHF, sex, age, concomitant pathology. The obtained data should be taken into account in personalized management of patients with CHF.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".