The Efficacy of Vitamins in the Prevention and Treatment of Cardiovascular Disease
Bibliographic record
Abstract
Vitamins are known to affect the regulation of several biochemical and metabolic pathways that influence cellular function. Adequate amounts of both hydrophilic and lipophilic vitamins are required for maintaining normal cardiac and vascular function, but their deficiencies can contribute to cardiovascular abnormalities. In this regard, a deficiency in the lipophilic vitamins, such as vitamins A, D, and E, as well as in the hydrophilic vitamins, such as vitamin C and B, has been associated with suboptimal cardiovascular function, whereas additional intakes have been suggested to reduce the risk of atherosclerosis, hypertension, ischemic heart disease, arrhythmias, and heart failure. Here, we have attempted to describe the association between low vitamin status and cardiovascular disease, and to offer a discussion on the efficacy of vitamins. While there are inconsistencies in the impact of a deficiency in vitamins on the development of cardiovascular disease and the benefits associated with supplementation, this review proposes that specific vitamins may contribute to the prevention of cardiovascular disease in individuals at risk rather than serve as an adjunct therapy.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".