Promotion of healthy nutrition in primary and secondary cardiovascular disease prevention: a clinical consensus statement from the European Association of Preventive Cardiology
Bibliographic record
Abstract
BACKGROUND: Poor dietary habits are common and lead to significant morbidity and mortality. However, addressing and improving nutrition in various cardiovascular settings remain sub-optimal. This paper discusses practical approaches to how nutritional counselling and promotion could be undertaken in primary care, cardiac rehabilitation, sports medicine, paediatric cardiology, and public health. DISCUSSION: Nutrition assessment in primary care could improve dietary patterns and use of e-technology is likely to revolutionize this. However, despite technological improvements, the use of smartphone apps to assist with healthier nutrition remains to be thoroughly evaluated. Cardiac rehabilitation programmes should provide individual nutritional plans adapted to the clinical characteristics of the patients and include their families in the dietary management. Nutrition for athletes depends on the sport and the individual and preference should be given to healthy foods, rather than nutritional supplements. Nutritional counselling is also very important in the management of children with familial hypercholesterolaemia and congenital heart disease. Finally, policies taxing unhealthy foods and promoting healthy eating at the population or workplace level could be effective for prevention of cardiovascular diseases. Within each setting, gaps in knowledge are provided. CONCLUSION: This clinical consensus statement contextualizes the clinician's role in nutrition management in primary care, cardiac rehabilitation, sports medicine, and public health, providing practical examples of how this could be achieved.
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 | 0.066 | 0.060 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.007 | 0.008 |
| Research integrity | 0.017 | 0.020 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".