The Preventive Potential of Common, Easily Measured Risk Factors for Cardiovascular Disease
Bibliographic record
Abstract
Featured Article: Yusuf S, Hawken S, Oounpuu S, Avezum A, Lanas F, McQueen M, et al. Effects of potentially modifiable risk factors associated with myocardial infarction in 52 countries (the INTERHEART study): case-control study. Lancet 2004; 64:937–52.3 In the 1990s mortality rates for cardiovascular disease (CVD)4 were declining in developed countries where the risk factors for acute myocardial infarction (AMI) had been studied. However, CVD was increasing in developing countries and more than 80% of global CVD was predicted to occur by 2020 in low- and middle-income countries, which would be challenged with improving their existing healthcare systems and simultaneously coping with an epidemic of CVD, previously not a healthcare problem for them. At that time known risk factors accounted for only 50% of CVD, implying a need to find new, probably more expensive, risk factors. Prevention first requires establishing appropriate and cost-effective preventive measures. Could risk factors derived mainly from North …
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.
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 | 0.007 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| 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".