Effective approaches to address the global cardiovascular disease burden
Bibliographic record
Abstract
PURPOSE OF REVIEW: Describe the global burden of cardiovascular disease (CVD), highlight barriers to evidence-based care and propose effective interventions based on identified barriers. RECENT FINDINGS: The global burden of CVD is increasing worldwide. This trend is steeper in lower income countries, where CVD incidence and fatality remains high. Risk factor control, around the world, remains poor, especially in lower and middle-income countries. Barriers at the patient, healthcare provider and health system have been identified. The use of multifaceted interventions that target identified contextual barriers to care, including increasing awareness of CVD and related risk, improving health policy (i.e. taxation of tobacco), improving the availability and affordability of fixed-dose combined medications and task-shifting of healthcare responsibilities are potential solutions to improve the global burden of CVD. SUMMARY: There is a need to address identified barriers using evidence-based and multifaceted interventions. Global initiatives, led by the World Heart Federation and the WHO, to facilitate the implementation of such interventions are underway.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".