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Contribution of vitamin K (phylloquinone) intake on warfarin therapy stability in elderly patients

2011· article· en· W3175809557 on OpenAlexaff
Guylaine Ferland, Cristina Leblanc, Nancy Presse

Bibliographic record

VenueThe FASEB Journal · 2011
Typearticle
Languageen
FieldNursing
TopicVitamin K Research Studies
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsMedicineVitamin kAnticoagulant therapyWarfarinAnalysis of varianceInternal medicineAnticoagulantCoagulationOral anticoagulantAtrial fibrillation

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.197
Threshold uncertainty score0.375

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.054
GPT teacher head0.289
Teacher spread0.235 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2011
Admission routes1
Has abstractyes

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