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Low Dose Oral Vitamin K Does Not Increase Time in Therapeutic Range: Results of a Multicentre Clinical Trial

2014· article· en· W2979345699 on OpenAlexaff
Mark Crowther, Luqi Wang, Alejandro Lazo Langer, Kovacs Michael, Erik Yeo, Terri Schnurr, Sam Schulman

Bibliographic record

VenueBlood · 2014
Typearticle
Languageen
FieldMedicine
TopicAtrial Fibrillation Management and Outcomes
Canadian institutionsThrombosis and Atherosclerosis Research InstituteWestern UniversitySt. Joseph’s Healthcare HamiltonUniversity of TorontoMcMaster UniversitySt. Joseph's Hospital
Fundersnot available
KeywordsWarfarinMedicinePlaceboClinical trialInternal medicineAnticoagulantOral anticoagulantRandomized controlled trialTherapeutic indexAcenocoumarolAtrial fibrillationPharmacologyDrug

Abstract

fetched live from OpenAlex

Abstract Warfarin remains the most commonly used oral anticoagulant. Its efficacy is closely correlated with the time in therapeutic range (TTR) of the international normalized ratio (INR). Excellent anticoagulant management systems can achieve TTRs in excess of 80%: however, in day-to-day clinical practice TTR values in the range of 50 to 60% are more commonly seen. Strategies to increase TTR are widely sought; one such intervention is the daily administration of low dose vitamin K (VK) administered to reduce variations in VK intake and thus reduce fluctuations in the INR due to such variability. In this multicentre, blinded, randomized controlled trial we allocated patients on chronic warfarin therapy to receive either 0.150 mg of daily oral VK, or matching placebo. After a one month “run in period” to allow adjustment of the warfarin dose, patients were followed for a maximum of 9 months (mean 6 months), their INR was determined, their TTR calculated and their mean warfarin dose calculated. A total of 253 patients were enrolled at 4 clinical centres between Aug 19, 2010 and Aug 30, 2013; 18 (9 in each group) were excluded from this analysis due to inadequate follow-up data, mostly as a result of withdrawal from the study during the run in period. Of the 117 analyzable patients allocated to VK, 66 were male (average age 66.9 yrs). Of the 118 analyzable patients allocated to placebo 59 were male (average age 65.7 yrs). Indications for warfarin were similar between the two groups, as was the frequency of renal insufficiency and the presence of cancer. Patients allocated to placebo were more likely to report a history of bleeding (7% vs 3%). Mean TTR over the 6 months prior to enrollment to the study was 54.6% in the placebo group, and 51.8% in the VK group. Mean TTR over an average follow-up of 6 months after completion of the 1 month run-in period was 66% in the placebo group, and 65.1% in the VK group. We conclude that inclusion in this study resulted in a statistically (and likely clinically) significant improvement in the TTR. The improvement in the TTR we observed appeared to be predominately as a result of attentive clinical management, rather than administration of VK. Our findings do not support the use of daily, low dose oral vitamin K administered in an effort to improve TTR. Disclosures Crowther: Portola: Consultancy; Leo: Consultancy, Honoraria, Research Funding; Bayer: Consultancy, Honoraria, Speakers Bureau; Janssen: Consultancy; AKP America: Consultancy; Heart and Stroke Foundation: Career Investigator award Other; Celgene: Honoraria; Shire: Honoraria; CSL Behring: Honoraria. Lazo Langer:Pfizer: Honoraria, Research Funding; Bayer: Honoraria, Research Funding; Leo Pharma: Honoraria, Research Funding; Boehringer Ingelheim: Honoraria; Alexion: Research Funding; Daichii Sankyo: Research Funding; Novartis: Research Funding; Celgene: Research Funding. Yeo:Bayer: Honoraria. Schulman:Boehringer Ingelheim: Consultancy, Honoraria, Research Funding; Bayer HealthCare: Consultancy, Honoraria, Research Funding.

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 imitation

Not 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.

metaresearch head score (Codex)0.010
metaresearch head score (Gemma)0.012
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Randomized trial · Consensus signal: Randomized trial
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.052

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.012
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0050.004
Bibliometrics0.0000.001
Science and technology studies0.0010.002
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0060.001

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.

Opus teacher head0.058
GPT teacher head0.362
Teacher spread0.304 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designRandomized trial
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
Published2014
Admission routes1
Has abstractyes

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