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Record W2016110271 · doi:10.1182/blood-2013-09-529784

How I treat target-specific oral anticoagulant–associated bleeding

2014· article· en· W2016110271 on OpenAlexaff
Deborah Siegal, David García, Mark Crowther

Bibliographic record

VenueBlood · 2014
Typearticle
Languageen
FieldMedicine
TopicAtrial Fibrillation Management and Outcomes
Canadian institutionsMcMaster University
Fundersnot available
KeywordsMedicineApixabanRivaroxabanOral anticoagulantWarfarinAnticoagulantInternal medicineGastroenterologySurgeryAtrial fibrillation

Abstract

fetched live from OpenAlex

Target-specific oral anticoagulants (TSOACs) that directly inhibit thrombin (dabigatran) or factor Xa (rivaroxaban, apixaban) are effective and safe alternatives to vitamin K antagonists (VKAs) and low-molecular-weight heparin (LMWH). Although these agents have practical advantages compared with VKAs and LMWH, there are no antidotes that reverse their anticoagulant effect. Clinical evidence for the efficacy of nonspecific therapies that promote formation of fibrin (prothrombin complex concentrate [PCC], activated PCC [aPCC], and recombinant factor VIIa) in the setting of TSOAC-associated bleeding is lacking, and these prohemostatic products are associated with a risk of thrombosis. In the absence of specific antidotes, addition of PCC or aPCC to maximum supportive therapy may be reasonable for patients with severe or life-threatening TSOAC-associated bleeding. Targeted antidotes for these agents are 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 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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.332
Threshold uncertainty score0.411

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.058
GPT teacher head0.286
Teacher spread0.228 · 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

Citations114
Published2014
Admission routes1
Has abstractyes

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