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Record W2132299631 · doi:10.1161/atvbaha.108.162677

New Anticoagulants for Treatment of Venous Thromboembolism

2008· review· en· W2132299631 on OpenAlexaff
Peter L. Gross, Jeffrey I. Weitz

Bibliographic record

VenueArteriosclerosis Thrombosis and Vascular Biology · 2008
Typereview
Languageen
FieldMedicine
TopicVenous Thromboembolism Diagnosis and Management
Canadian institutionsMcMaster University Medical Centre
Fundersnot available
KeywordsFondaparinuxMedicineWarfarinPulmonary embolismIntensive care medicineHeparinAnticoagulantRivaroxabanThrombusDiscovery and development of direct thrombin inhibitorsEdoxabanXimelagatranPhenprocoumonVenous thromboembolismVenous thrombosisLow molecular weight heparinThrombosisDabigatranThrombinSurgeryInternal medicineAtrial fibrillation

Abstract

fetched live from OpenAlex

Anticoagulant therapy is the cornerstone of treatment of venous thromboembolism (VTE). Such treatment is divided into 2 stages: Rapid initial anticoagulation is given to minimize the risk of thrombus extension and fatal pulmonary embolism, whereas extended anticoagulation is aimed at preventing recurrent VTE, thereby reducing the risk of postphlebitic syndrome. With currently available drugs, immediate anticoagulation can only be achieved with parenteral agents, such as heparin, low-molecular-weight heparin, or fondaparinux. Extended treatment usually involves the administration of vitamin K antagonists, such as warfarin. Emerging anticoagulants have the potential to streamline VTE treatment. These agents include idraparinux, a long-acting synthetic pentasaccharide that is given subcutaneously on a once-weekly basis, and new oral anticoagulants that target thrombin or factor Xa. This article (1) reviews the pharmacology of these agents, (2) outlines their potential strengths and weaknesses, (3) describes the results of clinical trials with these new drugs, and (4) identifies the evolving role of new anticoagulants in the management of VTE.

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: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.970
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0080.002
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
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 head0.128
GPT teacher head0.382
Teacher spread0.254 · 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 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

Citations176
Published2008
Admission routes1
Has abstractyes

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