The causal exposure model of vascular disease
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it