Genetics of Coronary Artery Disease in the 21st Century
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
Abstract Coronary artery disease (CAD) is still the number‐one killer in the world, and clinical trials indicate that it is preventable. Mortality and morbidity can be reduced by at least 30% to 40% by treating known risk factors. Genetic susceptibility is claimed to account for 50% of predisposition. The challenge of preventing CAD in this century, as claimed by some investigators, will require a more comprehensive prevention and treatment of environmental and genetic risk factors. Part of that challenge has been met by genome‐wide association studies, which have identified 36 genetic variants with increased risk for CAD. All of these genetic variants have reached genome‐wide significance (5×10 −8 ) and replicate in independent populations with large sample sizes. More than 50% of these variants occur in >50% of the population, with 10 occurring in >75% of the population. The challenge and the opportunity lie in the observation that >66% of these risk variants do not mediate their risk through known conventional risk factors. These results suggest that genetic predisposition for CAD is conferred by common DNA variants and many factors contributing to the pathogenesis of CAD are yet to be determined. Comprehensive prevention of CAD will most likely require combating genetic and environmental risk factors. We are on the cusp of genetic screening, and new therapeutic targets are becoming available to manage both genetic and environmental risk factors for CAD. The authors are supported by grants from the Canadian Institutes of Health Research, nos. MOP82810 (RR) and MOP77682 (AFRS), and the Canada Foundation for Innovation, no. 11966 (RR). The authors have no other funding, financial relationships, or conflicts of interest to disclose.
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 | 0.002 | 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 | 0.000 |
| 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