Cardiovascular disease in the Americas: the epidemiology of cardiovascular disease and its risk factors
Bibliographic record
Abstract
This first article of the Series about Cardiovascular Disease in the Americas summarizes the epidemiology of CVD and its risk factors, and population-level strategies in place aimed at CVD prevention. While age-standardized CVD incidence and CV mortality rates have been decreasing across in the Americas since 1990, the annual number of CVD cases and related deaths have increased due to population growth and ageing. The burden of CVD is also slowly transitioning from high-income countries in North America to middle-income countries in Latin America and the Caribbean. Trends in CV risk factor levels have been mixed, with declines in smoking and mean cholesterol counterbalanced by higher prevalence of obesity and diabetes. Population-wide strategies aimed at controlling cardiometabolic risk factors and tobacco use have been implemented with varying degrees of success. There is a need to better implement existing CVD prevention strategies in the region.
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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 | 0.011 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| 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".