Current Status of Primary, Secondary, and Tertiary Prevention of Coronary Artery Disease
Bibliographic record
Abstract
Fifty percent of all death from cardiovascular diseases is due to coronary artery disease (CAD). This is avoidable if early identification is made. Preventive health care has a major role in the fight against CAD. Atherosclerosis and atherosclerotic plaque rupture are involved in the development of CAD. Modifiable risk factors for CAD are dyslipidemia, diabetes, hypertension, cigarette smoking, obesity, chronic renal disease, chronic infection, high C-reactive protein, and hyperhomocysteinemia. CAD can be prevented by modification of risk factors. This paper defines the primary, secondary, and tertiary prevention of CAD. It discusses the mechanism of risk factor-induced atherosclerosis. This paper describes the CAD risk score and its use in the selection of individuals for primary prevention of CAD. Guidelines for primary, secondary, and tertiary prevention of CAD have been described. Modification of risk factors and use of guidelines for prevention of CAD would prevent, regress, and slow down the progression of CAD, improve the quality of life of patient, and reduce the health care cost.
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 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".