The Role of Risk Factors in the Development of Atherosclerosis
Bibliographic record
Abstract
Our understanding of risk factors for atherogenesis has changed significantly over the last decade. In addition to better grasp of the mechanism of action of the "classic" (causal) risk factors, a number of potentially important new factors has emerged. In this review we briefly summarize the evidence of the relation between atherosclerosis and the currently recognized causal risk factors, namely, age, smoking, LDL cholesterol, HDL cholesterol, hypertension, and diabetes. More emphasis has been put on description of the emerging entities such as atherogenic profile of plasma lipoproteins with discussion of LDL and HDL subclasses, Lp(a), homocysteine, and, last but not least, on the role of infection and inflammation in atherogenesis. Whenever possible, we tried to summarize the relevant lines of evidence such as epidemiological, pathological, genetic, and clinical trial data linking the specific factor with atherosclerosis.
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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 | 0.031 | 0.042 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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; both teacher heads agree on what is shown here.
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".