Study on the Correlation among Sleep Quality, Cognitive Function, and Self-Management Ability in Hospitalized Elderly Patients with Coronary Heart Disease
Bibliographic record
Abstract
Background. Coronary heart disease (CHD) is the leading cause of death worldwide. The incidence of cardiovascular disease is especially common in low-level and middle-income countries. With the increase in the number of patients with CHD and the complexity of treatment on patients with CHD, many hospitals are devoted to developing new models of care and management for patients with CHD. Understanding the unique characteristics of the patient’s condition, including factors related to self-management, cognitive function, and sleep quality, will lead to a substantial reduction in cardiovascular disease and related mortality. Objective. To investigate the correlation among sleep quality, cognitive function, and self-management ability in hospitalized elderly patients with coronary heart disease (CHD). Methods. 120 hospitalized elderly patients with coronary heart disease (CHD) were investigated by using a self-designed general data questionnaire, Pittsburgh Sleep Quality Index (PSQI), Montreal Cognitive Function Assessment (MoCA) scale, and Coronary Heart Disease Self-management Behavior Scale (CSMS). Results. The Spearman analysis showed that sleep quality was positively correlated with cognitive function in hospitalized elderly CHD patients ( P < 0.05 ). Sleep quality was positively correlated with self-management ability in CHD patients ( P < 0.05 ). Conclusion. Improving the cognitive function and self-management ability of elderly patients with coronary heart disease can improve their sleep quality.
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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) | 0.001 | 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".