Population-based versus high-risk strategies for the prevention of cardiovascular diseases in low- and middle-income countries
Bibliographic record
Abstract
Cardiovascular diseases (CVD) are now the number one cause of death in low- and middle-income countries (LMIC), such as those in South East Asia (SEA). It is projected that SEA countries will have the greatest total number of deaths due to non-communicable diseases (NCDs) by 2020. In low resource countries, the rising burden of CVDs imposes severe economic consequences that range from impoverishment of families to high health system costs and the weakening of country economies. There are two possible options to be considered for addressing this issue: a "population-based strategy" and/or a "high risk" strategy. The question is, what is the optimal way to reduce the excessive burden of these diseases in the LMICs. We believe that by applying systematic policy and smoking cessation programs with proven effectiveness, there is a chance that the high smoking prevalence, particularly among SEA.
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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 | 0.001 | 0.000 |
| 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 | 0.001 |
| 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".