Cardiac Rehabilitation Delivery in Low and Middle-Income Countries
Bibliographic record
Abstract
Cardiovascular diseases are among the leading causes of disability in low- and middle-income countries (LMICs). Cardiac rehabilitation (CR) is an effective secondary prevention program model. In this cross-sectional study, a confidential, online surveywas administered to CR programs around the world. CR programs were identified in 55/138 (39.9%) LMICs; 47 (85.5% country response rate) countries participated and 335(53.5% program response rate) surveys were initiated. There was 1 CR spot for every66 incident ischemic heart disease patients in LMICs. CR was most often paid by patients in LMICs (n=212,65.0%). On average, programs offered 7.31.8/11 core components over 33.730.7 sessions (significantly greater in publicly-funded programs;p<.001). Lack of patient referral (3.8/5) and financial resources (3.5/5) were the greatest barriers to CR provision in LMICs. CR is only available in 40% of LMICs, but where offered is fairly consistent with CR guidelines. Governments must enact policies to reimburse CR so patients do notCardiovascular diseases are among the leading causes of disability in low- and middle-income countries (LMICs). Cardiac rehabilitation (CR) is an effective secondary prevention program model. In this cross-sectional study, a confidential, online surveywas administered to CR programs around the world. CR programs were identified in 55/138 (39.9%) LMICs; 47 (85.5% country response rate) countries participated and 335(53.5% program response rate) surveys were initiated. There was 1 CR spot for every66 incident ischemic heart disease patients in LMICs. CR was most often paid by patients in LMICs (n=212,65.0%). On average, programs offered 7.31.8/11 core components over 33.730.7 sessions (significantly greater in publicly-funded programs;p<.001). Lack of patient referral (3.8/5) and financial resources (3.5/5) were the greatest barriers to CR provision in LMICs. CR is only available in 40% of LMICs, but where offered is fairly consistent with CR guidelines. Governments must enact policies to reimburse CR so patients do not pay out-of-pocket.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.004 |
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 teacher head, 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".