A Review on Coronary Heart Disease Prevention: The Role of Nutrition, Foods, Dietary Patterns, and Oral Health in the Saudi Population
Bibliographic record
Abstract
Coronary heart disease (CHD) is a leading cause of morbidity and mortality worldwide, and its prevalence is particularly high in Saudi Arabia, where lifestyle changes, including poor dietary habits and inadequate oral health, have significantly contributed to the rising burden of cardiovascular disease. This review examines the role of nutrition, dietary patterns, and oral health in preventing CHD in the Saudi population. It highlights the interrelationship between diet and oral health in the pathophysiology of CHD, emphasizing how nutrition can affect oral health and vice versa. The paper also discusses the importance of community-based interventions and policy recommendations to promote both heart-healthy eating and proper oral hygiene practices. The synergistic effect of these factors in reducing the risk of CHD is explored, alongside the critical role of healthcare professionals in delivering integrated care. The findings suggest that a holistic approach, addressing both dietary and oral health concerns, is essential for the effective prevention of CHD in Saudi Arabia. Further research and public health initiatives are necessary to implement such comprehensive strategies at a national level.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".