Cardiovascular disease in the Americas: optimizing primary and secondary prevention of cardiovascular disease
Bibliographic record
Abstract
While, many interventions can prevent cardiovascular disease (CVD), and its resulting morbidity or mortality, these are used sub-optimally in most countries. Therefore, health systems need to develop new approaches to ensure that proven CVD therapies are delivered widely. In this review, we describe five impactful implementation strategies which include: (1) Task shifting, (2) Use of mobile-Health (mHealth) support and virtual access to care, (3) simplified diagnostic and management algorithms for the prevention of CVD, (4) improving the use of combinations of medicines (i.e., polypill), and (5) patient engagement and role of patient-nominated peer support (i.e., treatment supporters). Adapting and tailoring these strategies to the local context in different settings in various countries in the Americas and the Caribbean can reduce the morbidity and mortality of CVD substantially.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".