The impact of angina and cardiac history on health-related quality of life and depression in coronary heart disease patients
Bibliographic record
Abstract
OBJECTIVE: To prospectively examine the contribution of angina and cardiac history to health-related quality of life (HRQoL) and depression in cardiac patients, over 6 months post-hospitalization. METHODS: Participants were myocardial infarction (MI), percutaneous coronary intervention (PCI) or coronary artery bypass graft (CABG) outpatients under the age of 70 years. One hundred and seventy-one patients consented to participate, with 121 patients being retained 6 months later (71% response rate). The impact of the patient's cardiac history and the presence of angina on physical, social and emotional HRQoL and depression was examined. RESULTS: At baseline, cardiac history was not significantly related to any of the dimensions of HRQoL or depression. At 6-month follow-up, cardiac history significantly predicted a higher level of depression, and angina was predictive of a significantly worse emotional, physical and social HRQoL and a higher level of depression. DISCUSSION: The presence of a cardiac history is associated with depression 6 months post-cardiac event, and angina is associated with both an adverse HRQoL and higher levels of depression. As past research has demonstrated that depression is a risk factor for mortality in patients with established heart disease, it is important from both a clinical and a research perspective to address these issues.
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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 | 0.004 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".