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Record W2074821794 · doi:10.1586/14779072.2015.1034692

Dabigatran for the prevention and treatment of thromboembolic disorders

2015· review· en· W2074821794 on OpenAlexaff
Andrés Enríquez, Adrián Baranchuk, Damian Redfearn, Christopher S. Simpson, Hoshiar Abdollah, Kevin Michael

Bibliographic record

VenueExpert Review of Cardiovascular Therapy · 2015
Typereview
Languageen
FieldMedicine
TopicAtrial Fibrillation Management and Outcomes
Canadian institutionsQueen's University
Fundersnot available
KeywordsMedicineDabigatranWarfarinDirect thrombin inhibitorIdarucizumabDiscovery and development of direct thrombin inhibitorsAtrial fibrillationRecombinant factor VIIaRivaroxabanStroke (engine)AnesthesiaIntensive care medicineInternal medicineThrombinPlatelet

Abstract

fetched live from OpenAlex

Dabigatran, an oral direct thrombin inhibitor, was the first of a new class of drugs referred to as non-vitamin K oral anticoagulants. Dabigatran is better than warfarin for stroke prevention in non-valvular atrial fibrillation (dose of 150 mg twice a day), non-inferior to enoxaparin for venous thromboembolism prevention after orthopedic surgery and non-inferior to warfarin in preventing recurrence after acute venous thromboembolism. The safety profile is similar to standard anticoagulants, with significant reduction observed in intracranial hemorrhage. Other advantages include a rapid onset of action and a predictable pharmacokinetic profile, allowing a fixed-dose regimen without the need for routine anticoagulation monitoring. In the event of bleeding, general support measures are recommended and if severe, the use of non-specific hemostatic agents such as prothrombin complex concentrates and recombinant factor VIIa must be considered. A specific reversal agent (idarucizumab) is in 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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.010
Threshold uncertainty score0.033

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0100.004

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 head0.146
GPT teacher head0.433
Teacher spread0.288 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations8
Published2015
Admission routes1
Has abstractyes

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