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Record W1978875615 · doi:10.1097/moh.0b013e328363c170

Treatment with novel oral anticoagulants

2013· review· en· W1978875615 on OpenAlexaff
Shari Ghanny, Mark Crowther

Bibliographic record

VenueCurrent Opinion in Hematology · 2013
Typereview
Languageen
FieldMedicine
TopicAtrial Fibrillation Management and Outcomes
Canadian institutionsSt. Joseph’s Healthcare HamiltonSt. Joseph's HospitalMcMaster University
Fundersnot available
KeywordsMedicineIntensive care medicineRivaroxabanHematologyInternal medicineWarfarinAtrial fibrillation

Abstract

fetched live from OpenAlex

PURPOSE OF REVIEW: To summarize data relevant to novel oral anticoagulants (nOACs), mainly apixaban, dabigatran and rivaroxaban, as alternatives to vitamin K antagonists (VKAs). RECENT FINDINGS: RE-LY was the first contemporaneous study to compare a nOAC, dabigatran, with dose-adjusted warfarin, for prevention of stroke and systemic embolism in atrial fibrillation. Since then multiple studies have compared nOACs to warfarin for acute (RE-COVER, RECOVER-II, EINSTEIN-DVT and EINSTEIN-PE) and extended treatment of venous thromboembolism (VTE) (AMPLIFY-EXT, RE-MEDY and RE-SONATE). Additional studies have examined stroke prevention in atrial fibrillation (ARISTOTLE and ROCKET-AF). We do not examine, in depth, use of nOACs in coronary artery disease. SUMMARY: nOACs are an acceptable alternative to VKAs in certain situations - these are at least as effective as warfarin for secondary prevention of VTE and for prevention of stroke and systemic embolism in patients with atrial fibrillation. These compare favorably with warfarin with respect to their rate of fatal and major bleeding. However, special attention should be given when using these drugs in certain patient populations, in particular in patients with renal insufficiency, those receiving additional antithrombotic therapy, those with questionable compliance, patients of child bearing potential and those with a high risk of gastrointestinal bleeding.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.985
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0030.000
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)0.0000.000

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 head0.391
GPT teacher head0.485
Teacher spread0.094 · 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.

Study designNot applicable
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

Citations28
Published2013
Admission routes1
Has abstractyes

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