The causal exposure model of vascular disease
Bibliographic record
Abstract
Primary prevention of cardiovascular disease is governed at present by the risk factor model for cardiovascular events, a model which is widely accepted by physicians and professional associations, but which has important limitations: most critically, that effective treatment to reduce arterial damage is often delayed until the age at which cardiovascular events become common. This delay means that many of the early victims of vascular disease will not be identified in time. This delay also allows atherosclerosis to develop and progress unchecked within the arterial tree with the result that the absolute effectiveness of preventive therapy is limited by the time it is eventually initiated. The causal exposure model of vascular disease is an alternative to the risk factor model for cardiovascular events. Whereas the risk factor model aims to identify and treat those at markedly increased risk of vascular events within the next decade, the causal exposure model of vascular disease aims to prevent events by treating the causes of the disease when they are identified. In the risk factor model, age is an independent non-modifiable risk factor and the predictive power of age far outweighs that of the other risk factors. In the causal exposure model, age is the duration of time the arterial wall is exposed to the causes of atherosclerosis: apoB (apolipoprotein B) lipoproteins, hypertension, diabetes and smoking. Preventing the development of advanced atherosclerotic lesions by treating the causes of vascular disease is the simplest, surest and most effective way to prevent clinical events.
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.039 | 0.006 |
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".