The Global Epidemic of Atherosclerotic Cardiovascular Disease
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 it