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Record W2278404185 · doi:10.1586/14779072.2015.1096779

Evaluating coagulation tests in patients with atrial fibrillation using direct oral anticoagulants

2015· review· en· W2278404185 on OpenAlexaff
Noel Chan, Vinai Bhagirath, Brian Dale, John W. Eikelboom

Bibliographic record

VenueExpert Review of Cardiovascular Therapy · 2015
Typereview
Languageen
FieldMedicine
TopicAtrial Fibrillation Management and Outcomes
Canadian institutionsPopulation Health Research Institute
Fundersnot available
KeywordsMedicineEdoxabanRivaroxabanApixabanDabigatranAtrial fibrillationWarfarinAnticoagulantDrugThrombolysisIntensive care medicineCoagulation testingDirect thrombin inhibitorAnticoagulant drugInternal medicineStroke (engine)CardiologyCoagulationPharmacologyMyocardial infarction

Abstract

fetched live from OpenAlex

Four direct oral anticoagulants (dabigatran, rivaroxaban, apixaban, edoxaban) have been shown to be at least as effective and safe as warfarin for the prevention of stroke in atrial fibrillation and the prevention and treatment of venous thromboembolism. Although they are administered in fixed doses without routine coagulation monitoring, measurement of anticoagulant effect or drug levels may be useful to determine if: anticoagulant effect is present in patients who are bleeding or require an urgent procedure or thrombolysis; levels are within usual on-therapy range in patients with recurrent thromboembolism during treatment; and levels are outside of the usual on-therapy range in patients with overdose or with extreme clinical characteristics. Traditional coagulation assays are widely available but lack sensitivity to detect clinically relevant anticoagulant effects, and lack accuracy in quantitating drug levels. Specific drug assays are less widely available but can accurately measure drug levels and should be preferred.

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)0.004
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 score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0030.003
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.228
GPT teacher head0.466
Teacher spread0.238 · 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

Citations1
Published2015
Admission routes1
Has abstractyes

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