Dyslipidemia and the Current State of Cardiovascular Disease: Epidemiology, Risk Factors, and Effect of Lipid Lowering
Bibliographic record
Abstract
Ischemic heart disease and stroke are the leading causes of death worldwide. Herein we review the burden, epidemiology, and risk factors for atherosclerotic cardiovascular disease (ASCVD). The focus of this review is on the current state of ASCVD in Canada, however, the findings regarding epidemiological trends are likely to be reflective of global trends, particularly in high-income countries, and the discussion regarding risk factors and lipid lowering is universally applicable. In Canada, the burden of death from ASCVD is second only to cancer deaths. There are major differences in disease burden related to sex, geography, and socioeconomic status. The major risk factors for ASCVD have been identified, although new and emerging risk factors are an active area of research. Recent developments such as polygenic risk scores provide potential to identify individuals at risk for ASCVD earlier in life and institute preventative measures. Dyslipidemia, and in particular elevated concentrations of low-density lipoprotein cholesterol and apolipoprotein B are a major cause of ASCVD. Therapies to lower low-density lipoprotein/apolipoprotein B levels are key components to treating and preventing ASCVD. Addressing the causal risk factors for ASCVD in a manner that comprehensively considers the clinical, social, and economic implications of prevention strategies will be essential to reduce the burden of ASCVD and improve outcomes for patients.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".