Global Guidance for Dyslipidaemia Management in Adults: A Scoping Review
Bibliographic record
Abstract
Objective: Dyslipidaemia is a leading preventable cause of global cardiovascular disease (CVD) burden, responsible for over four million deaths each year (1). A scoping review took a worldwide perspective and assessed recent World Health Organization (WHO) guidance documents, other national or regional guidelines, and randomized controlled trial evidence supporting dyslipidaemia management best practices. Methods: Review of WHO guidance documents included aspects of dyslipidaemia management, but none provided a detailed and comprehensive approach. Of 11 non-WHO national or regional dyslipidaemia guidelines, nine met pre-defined inclusion criteria and were reviewed in depth. Structured electronic searches of MEDLINE found 27 systematic reviews of randomized clinical trials supporting dyslipidaemia management guideline priority topics. Findings: This scoping review found overall consistency in the recommendations of regional and national dyslipidaemia guideline recommendations. Guidelines varied in terms of approach to assessing patient CVD risk and recommendations to treat with non-statin lipid-lowering therapy (LLT). Robust randomized trial evidence supports that a dyslipidaemia management guideline focuses on priority areas including approach to patient selection for LLT efficacy and safety, selection of initial LLT drugs and dose intensity, timing of LLT monitoring, and LLT management in specific high-risk groups (familial hypercholesterolemia, diabetes, chronic kidney disease, HIV and other inflammatory diseases, and older adults). Few regional or national guidelines provided practical implementation recommendations or cost-effectiveness assessments; less clinical trial evidence was found for the priority topic of equitable treatment. Conclusion: Taking a global perspective, this scoping review describes the scope and depth of the current evidence base informing best practice management of dyslipidaemia for the primary and secondary prevention of CVD.
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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 | 0.027 | 0.073 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.019 | 0.016 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.012 | 0.005 |
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".