Health-Related Quality of Life of Coronary Artery Disease Patients under Secondary Prevention: A Cross-Sectional Survey from South India
Bibliographic record
Abstract
BACKGROUND: Health-related quality of life (HRQOL) is emerging as an important outcome among patients with documented coronary artery disease (CAD). The primary objective of this study was to report the HRQOL of CAD patients under secondary prevention-related treatment and follow-up using the 36-Item Short Form (SF-36) tool. METHODS: This was an analytical cross-sectional survey done in a hospital/clinic setting. We recruited CAD patients 30 to 80 years old with 1 to 6 years of follow-up. Patients self-reported HRQOL using SF-36. RESULTS: We recruited 1206 patients, among whom 879 (72.9%) were male. The mean age of patients was 61.3 (9.6) years. Mean (± standard deviation) scores for physical functioning, role limitations due to physical health, pain, and general health were 66.48 ± 29.41, 78.96 ± 28.01, 80.96 ± 21.15, and 51.49 ± 20.19, respectively. The scores for role limitations due to emotional problems, energy/fatigue, emotional well-being, and social functioning were 76.62 ± 28.0, 66.18 ± 23.92, 76.91 ± 20.47, and 74.49 ± 23.55. In subgroup analysis, age, sex, type of CAD, and treatment showed no significant association with any of the 8 domains of QOL. In addition, hypertension and diabetes showed no significant association with the individual domains of HRQOL. CONCLUSION: Patients with coronary artery disease under secondary prevention-related treatment have suboptimal HRQOL under both physical and mental domains. The role of demographic factors, comorbidities, disease subtypes, and treatment options in modifying HRQOL among patients with CAD appears to be minimal.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".