Bibliographic record
Abstract
Cardiovascular disease (CVD) remains the leading cause of death worldwide. To date, decades of research has established LDL-C (low-density lipoprotein cholesterol) as a causal factor in the development of atherosclerotic CVD. Statin therapy, supported by a broad evidence base, has demonstrated its superior efficacy in reducing LDL-C and subsequent cardiovascular risk. It therefore currently forms the mainstay of lipid-lowering therapy as recommended by international guidelines. Statin therapy is indicated in the secondary prevention of atherosclerotic CVD, as well as genetic causes of dyslipidemia (such as familial hypercholesterolemia). Although this strategy targets those most at risk, it merely addresses those most susceptible and does not account for the fact that most cardiovascular events occur in those at moderate to low risk. In addition, there is evidence for use in primary prevention such as in those with diabetes mellitus, chronic kidney disease, and high risk of future atherosclerotic CVD as determined by risk prediction calculators. Risk prediction tools, however, are far from perfect and do not accurately account for those at low short-term but high lifelong risk. Considering the log-linear relationship between LDL-C reductions and reductions in risk of atherosclerotic CVD, even in those at very low risk of future events, a clinical question posed is can we and should we shift the entire risk distribution by treating everyone? The present review discusses these issues in more detail outlining arguments for and against each approach.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.002 |
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".