Contribution of vitamin K (phylloquinone) intake on warfarin therapy stability in elderly patients
Bibliographic record
Abstract
Warfarin (W) is a widely used oral anticoagulant and serves in the prevention of stroke and venous thromboembolism by blocking vitamin K (VK)‐dependent activation of coagulation factors. Despite its efficiency, stability of W therapy remains a daily challenge with bleeding a common side effect of the treatment. Although dietary VK has long been suggested to influence anticoagulant stability, data supporting its specific contribution remains limited. The present study was conducted to better understand the role of dietary VK as a determinant of W therapy stability. VK intake was assessed in 52 W‐treated elderly patients from an anticoagulation clinic using a validated food‐frequency questionnaire and compared to the stability of W therapy defined by a score (‘S’ score) based on the frequency of medical visits; higher ‘S’ scores indicating greater stability. VK was associated to stability of W therapy with patients consuming ≥200 μg/d VK showing more stability (p=0,019) than those with lower VK intakes. Patients who had been advised to ‘ Reduce their consumption of green vegetables' tended to have lower ‘S’ scores (ANOVA; p=0,057) and lower VK intakes than other patients (ANOVA; p=0,074). Results from this study support the notion that VK intake influences W anticoagulant therapy and that higher daily intakes contribute to more stable treatment. CL received a scholarship from Faculté de médecine, U de Mtl.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".