The Effectiveness of Cardiac Rehabilitation Programs in Improving Cardiovascular Outcomes: Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Cardiovascular diseases (CVDs) are some of the most common conditions and the major contributors to death and disability globally, hence the need for proper secondary prevention interventions. Cardiac rehabilitation (CR) programs have been recognized as an essential component in the treatment of CVDs with the goal of decreasing the risk of new cardiovascular events and improving the quality of life. This systematic review and meta-analysis sought to determine the impact of CR as a form of CVD treatment on mortality, morbidity, functional capacity, and quality of life amongst the patient population. The search resulted in 12 studies that fulfilled the inclusion criteria, which included both randomized controlled trials as well as cohort studies. The meta-analysis, therefore, showed that the CR program is effective in reducing all-cause mortality (RR=0 74, 95% CI: 0.62-0. Favorable effects of intervention regarding participation measures were found in the International Classification of Functioning, Disability and Health (ICF) domains of body functions (pool standardized mean differences (SMD)= 0.55, 95% CI: 0.43-0.68). The results confirm the significance of CR programs as an essential element of secondary prevention of CVDs, stressing the ability of CR to lower mortality rates and improve patients' functional status. Despite this, the implementation of CR programs continues to be suboptimal globally for various healthcare facilities; hence the requirement for interventions to ensure that more patients incorporate the protocols and adapt uniform CR protocols.
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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 | 0.011 | 0.027 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.035 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".