Drug Therapies in the Secondary Prevention of Cardiovascular Diseases:Successes, Shortcomings and Future Directions
Bibliographic record
Abstract
Cardiovascular diseases are the major cause of death and a significant cause of disability in the Western world and more recently threaten to pose an increasing health burden on developing nations. People with pre-existent vascular disease are those at highest risk for adverse cardiovascular outcomes and require aggressive secondary preventive therapies. Large strides have been made in the development of pharmacologic agents that intervene on various pathways implicated in atherogenesis, thus offering the ability to greatly impact on disease progression and to prevent events. Compelling data derived primarily from randomized controlled trials have shown the benefits of aspirin (or antiplatelet agents) and angiotensin converting enzyme (ACE) inhibitors (A), beta-blockers and blood pressure (B) and cholesterol-lowering drugs (C), particularly statins, in preventing recurrent events and improving survival. Taken together these data are the foundation for the simple, but important advice for secondary prevention - the ABCs. In addition, the evidence for the central role of lifestyle factors as determinants of risk has lead to increased efforts towards developing interventions aimed at modifying lifestyle patterns. Today, the biggest challenge remains in the implementation of proven effective therapies. Our focus should turn to educating physicians and patients alike regarding available therapies and their indications. In addition systematic, sustainable and globally applicable approaches to the secondary prevention of cardiovascular diseases need to be developed to truly realize the vast potential benefits of existing therapies.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".