Resource Effective Strategies to Prevent and Treat Cardiovascular Disease
Bibliographic record
Abstract
Cardiovascular disease (CVD) is the leading cause of global deaths, with the majority occurring in low- and middle-income countries. The primary and secondary prevention of CVD is suboptimal throughout the world, but the evidence-practice gaps are much more pronounced in low- and middle-income countries. Barriers at the patient, healthcare provider, and health system level prevent the implementation of optimal primary and secondary prevention. Identification of the particular barriers that exist in resource-constrained settings is necessary to inform effective strategies to reduce the identified evidence-practice gaps. Furthermore, targeting modifiable factors that contribute most significantly to the global burden of CVD, including tobacco use, hypertension, and secondary prevention for CVD, will lead to the biggest gains in mortality reduction. We review a select number of novel, resource-efficient strategies to reduce premature mortality from CVD, including (1) effective measures for tobacco control, (2) implementation of simplified screening and management algorithms for those with or at risk of CVD, (3) increasing the availability and affordability of simplified and cost-effective treatment regimens including combination CVD preventive drug therapy, and (4) simplified delivery of healthcare through task-sharing (nonphysician health workers) and optimizing self-management (treatment supporters). Developing and deploying systems of care that address barriers related to the above will lead to substantial reductions in CVD and related mortality.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".