World Heart Federation Cholesterol Roadmap
Bibliographic record
Abstract
BACKGROUND: The World Heart Federation has undertaken an initiative to develop a series of Roadmaps. OBJECTIVES: The aim of these is to promote development of national policies and health systems approaches and identify potential roadblocks on the road to effective prevention, detection and management of cardiovascular disease (CVD) in low-and middle-income countries (LMIC), and strategies for overcoming these. This Roadmap focuses on elevated blood cholesterol, a leading risk factor for myocardial infarction, stroke, and peripheral arterial disease. METHODS: Through a review of published guidelines and research papers, and consultation with a committee composed of experts in clinical management of cholesterol and health systems research in LMIC, this Roadmap identifies (1) key interventions for primordial, primary and secondary prevention of CVD through detection, treatment, and management of elevated cholesterol and familial hypercholesterolemia (FH); (2) gaps in implementation of these interventions (knowledge-practice gaps); (3) health system roadblocks to treatment of elevated cholesterol in LMIC; and (4) potential strategies for overcoming these. RESULTS: Despite strong evidence of the importance of cholesterol levels in primary or secondary prevention of CVD, and the effectiveness of statin therapy for cholesterol lowering and reduction of CVD risk, gaps exist in the detection, treatment, and management of high cholesterol globally. Some potential roadblocks include poor access to laboratory facilities or trained professionals for cholesterol management, low awareness of FH among the general population and health professionals, unaffordability of statins for patient households, and low awareness of the importance of persistent adherence to lipid-lowering medication. Potential solutions include point-of-care testing, provision of free or subsidized lipid-lowering medication, and treatment adherence support using text message reminders. CONCLUSIONS: Known effective strategies for detection, treatment, and management of elevated cholesterol and FH exist, but there are barriers to their implementation in many low-resource settings. Priorities for health system intervention should be identified at the national level, and the feasibility and effectiveness of proposed solutions should be assessed in specific contexts. Many solutions proposed in this Roadmap may apply to other cardiovascular conditions and present opportunities for integration of CVD care in LMIC.
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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 | 0.013 | 0.023 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.011 | 0.013 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.070 | 0.053 |
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".