Food Drug Interaction Between Anticoagulants And Vitamin- K Nutrient In CVD Patients And Their Overall Nutrition
Bibliographic record
Abstract
Cardiovascular disease (CVD) remains the leading cause of mortality worldwide, necessitating effective management strategies, including the use of anticoagulants. This paper explores the interactions between anticoagulants, particularly vitamin K antagonists (VKAs) like warfarin, and dietary sources of vitamin K, which can significantly impact treatment outcomes. CVD patients often supplement their diets with vitamins and nutrients, creating potential drug-nutrient interactions that can complicate anticoagulation therapy. Vitamin K, crucial for the synthesis of clotting factors, can diminish the efficacy of VKAs when consumed in high quantities, particularly from green leafy vegetables and fermented foods rich in vitamin K2. Additionally, we highlight dietary recommendations for CVD patients from organizations such as the American Heart Association, which advocate for balanced nutrition to support cardiovascular health. Understanding these interactions is vital for healthcare profession also to optimize anticoagulant therapy and improve patient outcomes in managing CVD. This review emphasizes the need for awareness of dietary influences on anticoagulant efficacy, promoting a holistic approach to patient care.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".