Relationships between Symptom Group and Quality of Life in Patients with Atrial Fibrillation: A Cross-Sectional Study
Bibliographic record
Abstract
To investigate the symptoms of patients with atrial fibrillation, and to explore the types of symptoms and their correlation with quality of life. Provide a basis for the quality of life with atrial fibrillation. Methods: A cross-sectional study was conducted between January 2020 to September 2021 on patients recruited from a tertiary hospital in Chengdu. The Medical Outcome Study 36-item Short Form (MOS SF-36), University of Toronto atrial fibrillation Severity Scale and personal information questionnaires was used as survey measurement. Participants with a physician diagnosis of AF, documented on medical records, were included in this study. Data was collected through a survey that was distributed to all eligible patients through face-to-face way. Results: There were slightly more female (65.1%) than male participants, with a mean (SD) age of 73.65 (11.08) years. Two symptom clusters were evident, including Fatigue symptoms cluster(shortness of breath at rest, shortness of breath with activity, exercise intolerance, fatigue at rest) and cardiac symptoms cluster (palpitations, dizziness，chest pain），There was a negative correlation between quality of life and the two symptom clusters. Multivariate linear analysis showed that fatigue symptoms cluster and cardiac symptoms cluster could significantly predict the quality of life of patients(P<0.01). Conclusion: Two unique atrial fibrillation symptom clusters were identified in this study population，which seriously affect the quality of life of patients. Nurses should timely evaluate patients' symptoms and actively manage patients' symptom clusters, so as to effectively improve the quality of life of patients.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".