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Record W2462190066 · doi:10.1055/s-2003-40671

Orally Active Direct Thrombin Inhibitors

2003· review· en· W2462190066 on OpenAlexaff
Jeffrey I. Weitz

Bibliographic record

VenueSeminars in Vascular Medicine · 2003
Typereview
Languageen
FieldMedicine
TopicVenous Thromboembolism Diagnosis and Management
Canadian institutionsMcMaster University Medical Centre
Fundersnot available
KeywordsXimelagatranMedicineProdrugDiscovery and development of direct thrombin inhibitorsDosingDirect thrombin inhibitorPharmacologyAnticoagulantIntensive care medicineThrombinDrugWarfarinDabigatranSurgeryAtrial fibrillationInternal medicine

Abstract

fetched live from OpenAlex

Anticoagulants are widely used for the prevention and treatment of venous and arterial thrombosis. Current treatment strategies often employ a combination of parenteral and oral agents because the only available orally active anticoagulants, vitamin K antagonists, have a delayed onset of action. Furthermore, vitamin K antagonists have a narrow therapeutic window that necessitates careful anticoagulation monitoring, and dosing is problematic because of multiple food and drug interactions. These limitations highlight the need for oral anticoagulants that produce a more predictable anticoagulant response than vitamin K antagonists, thereby obviating the need for laboratory monitoring. Ximelagatran has the potential to meet this need. A prodrug of melagatran, an agent that targets thrombin, ximelagatran exhibits many of the characteristics of an ideal anticoagulant. This article (1). reviews the limitations of vitamin K antagonists, (2). lists the characteristics of an ideal anticoagulant, (3). rationalizes thrombin as a target for new anticoagulants, (4). reviews the preclinical and clinical data with ximelagatran, and (5). provides clinical perspective as to the future of ximelagatran and other orally active anticoagulants currently under development.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.929
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0070.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.334
Teacher spread0.303 · 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

Citations3
Published2003
Admission routes1
Has abstractyes

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