Reducing exposure to cardiovascular risk factors: the legacy of prevention
Bibliographic record
Abstract
Over 50 years ago, the Framingham Heart Study investigators identified a range of common risk factors associated with atherosclerotic cardiovascular disease (ACVD) (1). Since then, several large-scale epidemiological studies conducted across all continents, such as the INTERHEART study, have linked ACVD risk factor exposure to cardiovascular outcomes (2). It is now commonly accepted that ACVD is mostly caused by measurable risk factors and the vast majority of these are modifiable, either via lifestyle modification, pharmacotherapy, or both. Of all ACVD risk factors, circulating cholesterol levels have perhaps received the most attention from not only the scientific and medical communities, but also by the media and the public alike. The association between cholesterol levels and ACVD has been studied for more than a century, with the cholesterol hypothesis having had its ups and downs [as recently summarized by Pedersen (3)].
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 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 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.023 | 0.037 |
| Insufficient payload (model declined to judge) | 0.006 | 0.005 |
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".