Bibliographic record
Abstract
PURPOSE OF REVIEW: Prevention of coronary artery disease (CAD) is an appropriate goal for the 21st century. Randomized clinical studies consistently show a 30-40% reduction in mortality and morbidity by modifying known risk factors. However, genetic risk, estimated to account for 40-60% of susceptibility to CAD, has until recently been unknown. Comprehensive prevention will require knowledge of both. RECENT FINDINGS: The 21st century technology has responded to the challenge. Whereas the first genetic risk variant was not discovered until 2007 (9p21), a total of 36 genetic risk factors for CAD have been discovered and verified in large sample sizes. A startling discovery was that over two-thirds of these factors do not act through known risk factors or mechanisms. This obviously has great implications for the pathogenesis of CAD and presents many potential targets for new therapy. These genetic risk factors occur more commonly in the population than expected, with over half of them occurring in more than 50% of the population, and 10 of them occurring in at least 75% of the population. SUMMARY: The role of genetic risk factors in genetic screening for prevention of heart disease is yet to be defined. The technology is already available, but functional analysis may be a prerequisite for their clinical application.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".