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Record W2557845780 · doi:10.1161/atvbaha.115.303397

Overview of the New Oral Anticoagulants

2015· review· en· W2557845780 on OpenAlexafffund
Calvin H. Yeh, Kerstin Hogg, Jeffrey I. Weitz

Bibliographic record

VenueArteriosclerosis Thrombosis and Vascular Biology · 2015
Typereview
Languageen
FieldMedicine
TopicAtrial Fibrillation Management and Outcomes
Canadian institutionsThrombosis and Atherosclerosis Research Institute
FundersCanadian Institutes of Health ResearchMcMaster UniversityHeart and Stroke Foundation of Canada
KeywordsRivaroxabanApixabanEdoxabanDabigatranMedicineWarfarinIntensive care medicineAtrial fibrillationInternal medicine

Abstract

fetched live from OpenAlex

The non-vitamin K antagonist oral anticoagulants (NOACs) are replacing warfarin for many indications. These agents include dabigatran, which inhibits thrombin, and rivaroxaban, apixaban, and edoxaban, which inhibit factor Xa. All 4 agents are licensed in the United States for stroke prevention in atrial fibrillation and for treatment of venous thromboembolism and rivaroxaban and apixaban are approved for thromboprophylaxis after elective hip or knee arthroplasty. The NOACs are at least as effective as warfarin, but are not only more convenient to administer because they can be given in fixed doses without routine coagulation monitoring but also are safer because they are associated with less intracranial bleeding. As part of a theme series on the NOACs, this article (1) compares the pharmacological profiles of the NOACs with that of warfarin, (2) identifies the doses of the NOACs for each approved indication, (3) provides an overview of the completed phase III trials with the NOACs, (4) briefly discusses the ongoing studies with the NOACs for new indications, (5) reviews the emerging real-world data with the NOACs, and (6) highlights the potential opportunities for the NOACs and identifies the remaining challenges.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.880
Threshold uncertainty score0.701

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.404
GPT teacher head0.453
Teacher spread0.050 · 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 designOther design
Domainnot available
GenreReview

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

Citations189
Published2015
Admission routes2
Has abstractyes

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