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Record W3139166757 · doi:10.1055/a-1450-8178

2021 Update of the International Council for Standardization in Haematology Recommendations for Laboratory Measurement of Direct Oral Anticoagulants

2021· review· en· W3139166757 on OpenAlexaff
Jonathan Douxfils, Dorothy Adcock, Shannon M. Bates, Emmanuel J. Favaloro, Isabelle Gouin‐Thibault, Cecilia Guillermo, Yohko Kawai, Edelgard Lindhoff‐Last, Steve Kitchen, Robert C. Gosselin

Bibliographic record

VenueThrombosis and Haemostasis · 2021
Typereview
Languageen
FieldMedicine
TopicAtrial Fibrillation Management and Outcomes
Canadian institutionsMcMaster University Medical Centre
FundersGrifolsCSL BehringBayerPfizerDaiichi-SankyoBristol-Myers Squibb
KeywordsApixabanRivaroxabanMedicineStandardizationFood and drug administrationDabigatranExpert opinionIntensive care medicineMedical physicsPharmacologyInternal medicineComputer scienceWarfarinAtrial fibrillation

Abstract

fetched live from OpenAlex

In 2018, the International Council for Standardization in Haematology (ICSH) published a consensus document providing guidance for laboratories on measuring direct oral anticoagulants (DOACs). Since that publication, several significant changes related to DOACs have occurred, including the approval of a new DOAC by the Food and Drug Administration, betrixaban, and a specific DOAC reversal agent intended for use when the reversal of anticoagulation with apixaban or rivaroxaban is needed due to life-threatening or uncontrolled bleeding, andexanet alfa. In addition, this ICSH Working Party recognized areas where additional information was warranted, including patient population considerations and updates in point-of-care testing. The information in this manuscript supplements our previous ICSH DOAC laboratory guidance document. The recommendations provided are based on (1) information from peer-reviewed publications about laboratory measurement of DOACs, (2) contributing author's personal experience/expert opinion and (3) good laboratory practice.

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)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.950
Threshold uncertainty score0.488

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
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 head0.510
GPT teacher head0.444
Teacher spread0.066 · 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 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

Citations200
Published2021
Admission routes1
Has abstractyes

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