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New Anticoagulants

2010· review· en· W4245765453 on OpenAlexafffundabout
John W. Eikelboom, Jeffrey I. Weitz

Bibliographic record

VenueCirculation · 2010
Typereview
Languageen
FieldMedicine
TopicVenous Thromboembolism Diagnosis and Management
Canadian institutionsMcMaster University Medical Centre
FundersHeart and Stroke Foundation of Canada
KeywordsMedicineIntensive care medicine

Abstract

fetched live from OpenAlex

T hromboembolism involving the arterial or venous circu- lation or arising from the heart is a common cause of morbidity and mortality. Rapidly acting parenteral anticoagulants, such as heparin, are used for the prevention and initial treatment of thromboembolism and during revascularization procedures, 1 whereas the slower-acting vitamin K antagonists (VKAs) are used for long-term therapy. Development of new oral anticoagulants to replace VKAs has been slower than that of parenteral agents. Ximelagatran, an oral thrombin inhibitor, was briefly licensed in Europe but was withdrawn in 2006 because of potential hepatic toxicity. lthough this set the field back for several years, the situation has changed with the recent introduction of dabigatran etexilate, a new oral thrombin inhibitor, and rivaroxaban, an oral factor Xa (fXa) inhibitor. Licensed in Europe and Canada for prevention of venous thromboembolism (VTE) in patients undergoing hip or knee arthroplasty, dabigatran etexilate and rivaroxaban streamline out-of-hospital thromboprophylaxis because the drugs can be given once daily in fixed doses without coagulation monitoring. The greater unmet medical need, however, is to find a replacement for VKAs for long-term therapy, particularly stroke prevention in patients with atrial fibrillation (AF). The results of the Randomized Evaluation of Long-Term Anticoagulant Therapy (RE-LY) trial, which compared dabigatran etexilate with warfarin for stroke prevention in patients with AF, demonstrate that the new oral anticoagulants have the potential to be more effective and safer than VKAs.

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 score0.990
Threshold uncertainty score0.847

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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)0.0000.001

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 head0.067
GPT teacher head0.361
Teacher spread0.295 · 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

Citations243
Published2010
Admission routes3
Has abstractyes

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