Cardiovascular disease essential medicines listing by countries: changes over time and association with health outcomes
Bibliographic record
Abstract
BACKGROUND: Since national essential medicine lists guide the procurement of medicines for populations in many countries, and cardiovascular diseases are the leading cause of death globally, including cardiovascular medicines on these lists can significantly impact healthcare outcomes. METHODS: In this cross-sectional study, national essential medicines' lists from 158 countries were analysed on whether or not they included medicines to treat ischemic heart disease, cerebrovascular disease, and hypertensive heart disease. A linear regression model was used to evaluate the association between countries' coverage scores and amenable mortality. RESULTS: Listing of cardiovascular disease treatment was associated with amenable mortality from hypertensive heart disease. Health expenditure per capita was also associated with amendable mortality due to ischemic heart disease, and hypertensive heart disease. CONCLUSIONS: Listing essential medicines for cardiovascular disease is an important aspect of healthcare quality that is associated with cardiovascular mortality.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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".