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Record W2563678449 · doi:10.1093/eurheartj/suw049

What do the guidelines suggest for non-vitamin K antagonist oral anticoagulant use for stroke prevention in atrial fibrillation?

2016· article· en· W2563678449 on OpenAlexaboutno aff
Farhan Shahid, Eduard Shantsila, Gregory Y.H. Lip

Bibliographic record

VenueEuropean Heart Journal Supplements · 2016
Typearticle
Languageen
FieldMedicine
TopicAtrial Fibrillation Management and Outcomes
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineAtrial fibrillationWarfarinStroke (engine)Vitamin K antagonistIntensive care medicineVitamin kEdoxabanRivaroxabanInternal medicineCardiology

Abstract

fetched live from OpenAlex

Vitamin K antagonists (VKAs, e.g. Warfarin) have been the cornerstone of stroke prevention in patients with non-valvular atrial fibrillation (AF) for well over 50 years, being highly efficacious in reducing stroke and mortality in this common arrhythmia. More recent data have shown the relative efficacy, safety, and convenience of the non-VKA oral anticoagulants (NOACs) over warfarin in patients with AF. Guidelines throughout Europe, America, and Canada acknowledge the value of NOACs and many recommend their use as first-line therapy, sometimes preferentially to warfarin. With the recent availability of reversal agents, there is little reason not to prescribe NOACs where appropriate. This article provides an overview of the current international guidelines with regard to NOAC use and highlights key areas by which emerging evidence may change the management of stroke prevention in patients with non-valvular AF.

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)0.015
metaresearch head score (Gemma)0.065
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: Not applicable
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.015
Threshold uncertainty score0.080

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0150.065
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.003
Bibliometrics0.0040.005
Science and technology studies0.0020.004
Scholarly communication0.0060.008
Open science0.0050.002
Research integrity0.0110.015
Insufficient payload (model declined to judge)0.0070.005

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 head0.170
GPT teacher head0.424
Teacher spread0.254 · 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

Citations3
Published2016
Admission routes1
Has abstractyes

Explore more

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