MétaCan
Menu
Back to cohort

New anticoagulants and the management of their bleeding complications

2006· article· en· W2124295873 on OpenAlexaff
Heng Joo Ng, Mark Crowther

Bibliographic record

VenueTransfusion Alternatives in Transfusion Medicine · 2006
Typearticle
Languageen
FieldMedicine
TopicVenous Thromboembolism Diagnosis and Management
Canadian institutionsMcMaster University
Fundersnot available
KeywordsArgatrobanMedicineFondaparinuxBivalirudinDabigatranXimelagatranHirudinTissue factorRecombinant factor VIIaDiscovery and development of direct thrombin inhibitorsDirect thrombin inhibitorRivaroxabanAnticoagulantThrombinAntithrombinsPharmacologyAntithrombinHeparinAnesthesiaCoagulationWarfarinSurgeryThrombosisImmunologyInternal medicineVenous thromboembolismAtrial fibrillationMyocardial infarction

Abstract

fetched live from OpenAlex

SUMMARY Limitations of the currently available anticoagulants have fanned the continuing search for new anticoagulants with improved pharmacological and biosafety profile, and equal, if not superior efficacy. Targets of inhibition include the factor VIIa/tissue factor pathway (recombinant nematode anticoagulant peptide c2, tissue factor pathway inhibitor), factor Xa (fondaparinux, idraparinux, razaxaban), factor Va and VIIIa pathway (recombinant activated protein C, soluble thrombomodulin) and thrombin (hirudin, bivalirudin, argatroban, ximelagatran, dabigatran). Irrespective of their mode of action, bleeding complications are invariable with all anticoagulants. Conventional assessment and measures should remain as first‐line responses to bleeding complicating the use of these anticoagulants. Antidotes do not exist for the overwhelming majority of these agents. The role of recombinant activated factor VIIa in controlling bleeding is still investigational. Definitive haemostatic strategies for bleeding complications can only evolve with accumulating experience with these new agents.

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)0.001
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 score0.885
Threshold uncertainty score0.638

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.022
GPT teacher head0.296
Teacher spread0.274 · 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

Citations4
Published2006
Admission routes1
Has abstractyes

Explore more

Same venueTransfusion Alternatives in Transfusion MedicineSame topicVenous Thromboembolism Diagnosis and ManagementFrench-language works237,207