Comparative Effectiveness of the Core Components of Cardiac Rehabilitation on Mortality and Morbidity: A Systematic Review and Network Meta-Analysis
Bibliographic record
Abstract
A systematic review and network meta-analysis (NMA) of randomized controlled trials (RCTs) evaluating the core components of cardiac rehabilitation (CR), nutritional counseling (NC), risk factor modification (RFM), psychosocial management (PM), patient education (PE), and exercise training (ET)) was undertaken. Published RCTs were identified from database inception dates to April 2017, and risk of bias assessed using Cochrane's tool. Endpoints included mortality (all-cause and cardiovascular (CV)) and morbidity (fatal and non-fatal myocardial infarction (MI), coronary artery bypass surgery (CABG), percutaneous coronary intervention (PCI), and hospitalization (all-cause and CV)). Meta-regression models decomposed treatment effects into the main effects of core components, and two-way or all-way interactions between them. Ultimately, 148 RCTs (50,965 participants) were included. Main effects models were best fitting for mortality (e.g., for all-cause, specifically PM (hazard ratio HR = 0.68, 95% credible interval CrI = 0.54⁻0.85) and ET (HR = 0.75, 95% CrI = 0.60⁻0.92) components effective), MI (e.g., for all-cause, specifically PM (hazard ratio HR = 0.76, 95% credible interval CrI = 0.57⁻0.99), ET (HR = 0.75, 95% CrI = 0.56⁻0.99) and PE (HR = 0.68, 95% CrI = 0.47⁻0.99) components effective) and hospitalization (e.g., all-cause, PM (HR = 0.76, 95% CrI = 0.58⁻0.96) effective). For revascularization (including CABG and PCI individually), the full interaction model was best-fitting. Given that each component, individual or in combination, was associated with mortality and/or morbidity, recommendations for comprehensive CR are warranted.
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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 | 0.029 | 0.064 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.024 | 0.049 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".