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Record W2102216347 · doi:10.1177/2040620712453067

Advances in oral anticoagulation treatment: the safety and efficacy of rivaroxaban in the prevention and treatment of thromboembolism

2012· article· en· W2102216347 on OpenAlexaff
Alexander G.G. Turpie

Bibliographic record

VenueTherapeutic Advances in Hematology · 2012
Typearticle
Languageen
FieldMedicine
TopicVenous Thromboembolism Diagnosis and Management
Canadian institutionsMcMaster UniversityHamilton Health SciencesHamilton General Hospital
FundersBayer HealthCare
KeywordsMedicineRivaroxabanDabigatranApixabanEdoxabanDeep veinPulmonary embolismAtrial fibrillationThrombosisIntensive care medicineWarfarinSurgeryInternal medicine

Abstract

fetched live from OpenAlex

Arterial and venous thromboembolic diseases are a clinical and economic burden worldwide. In addition to traditional agents such as vitamin K antagonists and heparins, newer oral agents - such as the factor Xa inhibitors rivaroxaban, apixaban, and edoxaban, and the direct thrombin inhibitor dabigatran - have been shown to be effective across several indications. Rivaroxaban has been shown to have predictable pharmacokinetic and pharmacodynamic properties, including a rapid onset of action. In addition, there is no requirement for routine coagulation monitoring; and no dose adjustment is necessary for age alone, sex, or body weight. Rivaroxaban has successfully met primary efficacy and safety endpoints in large, randomized phase III trials across several indications, including: prevention of venous thromboembolism in orthopedic patients undergoing elective hip or knee replacement surgery; treatment of deep vein thrombosis and secondary prevention of deep vein thrombosis and pulmonary embolism; stroke prevention in patients with atrial fibrillation; and secondary prevention of acute coronary syndrome. Rivaroxaban and the other newer oral anticoagulants are likely to improve outcomes in the prevention and treatment of thromboembolic events, and will offer patients and physicians alternative treatment options.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.543
Threshold uncertainty score0.350

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)0.0000.000

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 head0.031
GPT teacher head0.351
Teacher spread0.320 · 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

Citations18
Published2012
Admission routes1
Has abstractyes

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