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Record W2778270325 · doi:10.1016/j.bja.2017.11.082

Emergency care of patients receiving non-vitamin K antagonist oral anticoagulants

2017· review· en· W2778270325 on OpenAlexaff
John W. Eikelboom, S. Kozek-Langenecker, A. Exadaktylos, Angelika Bátorová, Z Boda, F. Christory, Ivan Gornik, Gintautas Kėkštas, A Kher, Radko Komadina, Olena Koval, Gorana Mitić, T Novikova, E. Pazvanska, Svetlana Ratobilska, J. Sütt, Asher Winder, D. A. Zateyshchikov

Bibliographic record

VenueBritish Journal of Anaesthesia · 2017
Typereview
Languageen
FieldMedicine
TopicAtrial Fibrillation Management and Outcomes
Canadian institutionsHamilton Health SciencesPopulation Health Research InstituteMcMaster University
FundersSanofiGlaxoSmithKlinePfizerBristol-Myers Squibb
KeywordsDabigatranRivaroxabanApixabanEdoxabanMedicineVitamin K antagonistIntensive care medicineAnticoagulantVitamin kProthrombin complex concentrateAnesthesiaWarfarinSurgeryAtrial fibrillationInternal medicine

Abstract

fetched live from OpenAlex

Non-vitamin K antagonist oral anticoagulants (NOACs), which inhibit thrombin (dabigatran) and factor Xa (rivaroxaban, apixaban, edoxaban) have been introduced in several clinical indications. Although NOACs have a favourable benefit-risk profile and can be used without routine laboratory monitoring, they are associated-as any anticoagulant-with a risk of bleeding. In addition, treatment may need to be interrupted in patients who need surgery or other procedures. The objective of this article, developed by a multidisciplinary panel of experts in thrombosis and haemostasis, is to provide an update on the management of NOAC-treated patients who experience a bleeding episode or require an urgent procedure. Recent advances in the development of targeted reversal agents are expected to help streamline the management of NOAC-treated patients in whom rapid reversal of anticoagulation is required.

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)0.000
metaresearch head score (Gemma)0.002
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 score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
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 head0.094
GPT teacher head0.384
Teacher spread0.290 · 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

Citations124
Published2017
Admission routes1
Has abstractyes

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