Home-Based Secondary Prevention Programs for Patients With Coronary Artery Disease
Bibliographic record
Abstract
PURPOSE: Anxiety is common among patients with coronary artery disease (CAD). Despite the benefits of home-based CAD prevention interventions on quality of life and atherosclerotic risk factors, the efficacy of home-based programs in reducing patient anxiety is unknown. METHODS: We performed a systematic review and meta-analysis of all randomized trials that examined the effects of home-based interventions on anxiety reduction in patients with CAD published in 18 databases until December 2009. Analyses were based on changes in the standardized mean difference between treatment groups. RESULTS: Eight trials containing intervention means and standard deviations on anxiety were reviewed. Overall quality of the trials was low to moderate. Compared with usual care or center-based cardiac rehabilitation, home-based interventions had a small but significant effect in reducing anxiety (total effect size: -0.13; 95% CI: -0.20 to -0.06; P < .001; I = 66%). CONCLUSIONS: This meta-analysis provides evidence that home-based secondary prevention programs are effective in reducing anxiety level in CAD patients. However, because of the limited number of trials available and high degrees of heterogeneity in the data, further research needs to be done to provide a definitive answer on the benefits of home-based programs on anxiety management in CAD patients.
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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.007 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".