Effects of cluster nursing on cardiac function and quality of life in coronary heart disease patients with chronic heart failure
Bibliographic record
Abstract
BACKGROUND: Coronary heart disease (CHD) chronic heart failure has high morbidity and mortality, which poses a serious threat to patients' quality of life and life safety. For the treatment of chronic heart failure of CHD, in addition to drugs, high quality nursing measures are also very important. Cluster nursing is a high-quality nursing model based on evidence-based evidence. There is no clinical study to evaluate the effect of cluster nursing on cardiac function and quality of life of CHD patients with chronic heart failure. METHODS: This is a prospective randomized controlled trial to investigate the effects of cluster nursing on cardiac function and quality of life in patients with CHD chronic heart failure. Approved by the Clinical Research Ethics Committee of our hospital, patients will be randomly assigned to either routine nursing or cluster nursing. They will be followed up for 3 months after 4 weeks of treatment. Observation indicators include: The total effective rate of cardiac function improvement, Minnesota Living with Heart Failure Questionnaire, left ventricular ejection fraction, N-terminal pro-brain natriuretic peptide, 6-minute walk test, adverse reaction, etc. Data were analyzed using the statistical software package SPSS version 25.0. DISCUSSION: This study will evaluate the effects of cluster nursing on cardiac function and quality of life of CHD patients with chronic heart failure. The results of this study will provide clinical basis for establishing reasonable and effective nursing programs for CHD patients with chronic heart failure.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".