The future of education in Preventive Cardiology: a statement of the European Association of Preventive Cardiology of the European Society of Cardiology
Bibliographic record
Abstract
In recent years, major advances in our understanding of risk factors implicated in the development of cardiovascular disease (CVD), in available tools for early detection of CVD, and in effective interventions to prevent subclinical or clinically manifest disease, have led to an increasing appreciation of prevention as a major pillar of cardiovascular (CV) medicine. Preventive Cardiology has evolved into a dynamic sub-speciality focused on the promotion of CV health through all stages of life, and on the management of individuals at risk of developing CVD or experiencing recurrent CV events, through interdisciplinary care in different settings. As the level of knowledge, specialized skills, experience, and committed attitudes related to CV prevention has exceeded core cardiology training, the European Association of Preventive Cardiology (EAPC) has placed major emphasis on continuous education and training of physicians and allied professionals involved in CV prevention, with the aim of setting standards for practice and improving quality of care. The EAPC recognizes the need for a comprehensive educational offer across different levels of training (from core cardiology to sub-speciality to expert training) as well as the need for interdisciplinary approaches that will promote synergies among allied professionals involved in CV prevention. This statement by the EAPC aims to highlight current gaps and unmet needs and to describe the framework to help standardize, structure, and deliver comprehensive, up-to-date, interactive, and high-quality education using a combination of traditional and novel educational tools. The document aims to form the basis for ongoing refinements of the EAPC educational offer, with the ultimate goal of ensuring that new evidence in the field will translate to better CV practice and improved outcomes for our patients.
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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 | 0.043 | 0.038 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.027 | 0.035 |
| Insufficient payload (model declined to judge) | 0.003 | 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".