The Causal-Benefit Model to Prevent Cardiovascular Events
Bibliographic record
Abstract
Selecting individuals for preventive lipid-lowering therapy is presently governed by the 10-year risk model. Once a prespecified level of cardiovascular disease risk is equaled or exceeded, individuals become eligible for preventive lipid-lowering therapy. A key limitation of this model is that only a small minority of individuals below the age of 65 years are eligible for therapy. However, just under one-half of all cardiovascular disease events occur below this age. Additionally, in many, the disease that caused their events after 65 years of age developed and progressed before 65 years of age. The causal-benefit model of prevention identifies individuals based both on their risk and the estimated benefit from lowering atherogenic apoB lipoprotein levels. Adopting the causal-benefit model would increase the number of younger subjects eligible for preventive treatment, would increase the total number of cardiovascular disease events prevented at virtually the same number to treat, and would be cost-effective.
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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 | 0.008 | 0.017 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.028 | 0.003 |
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".