The Global Epidemic of Atherosclerotic Cardiovascular Disease
Bibliographic record
Abstract
Of the 50 million deaths that occur in the world, 40 million occur in developing countries. Already a substantial proportion of these deaths are due to cardiovascular diseases. It is projected that by the year 2025 well over 80-90% of all the cardiovascular diseases in the world will be occurring in low income and middle income countries. This increase in cardiovascular disease is due to a number of causes which include the following: (1) conquest of deaths in childhood and infancy from nutritional deficiencies and infection; (2) urbanization with increasing levels of obesity; (3) increasing longevity of the population so that a higher proportion of individuals reach the age when they are subject to chronic diseases, and (4) increasing use of tobacco worldwide. In most countries in the world other than those in the West, the burden of disease is still due to a combination of infections and nutritional disorders as well as those due to chronic diseases. This double burden of disease poses a challenge that is not only medical and epidemiological, but also social and political. Tackling this projected global epidemic of cardiovascular disease therefore needs policies that combine sound knowledge of prevention, good clinical care, but also deals with the allocation of resources for both individual level and community level preventive strategies. The former involves dealing with high-risk individuals through appropriate medical and therapeutic interventions. The latter involves societal level changes including laws that curb the use of tobacco, and strategies that promote physical activities, and appropriate nutrition.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 0.007 |
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".