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Record W4388609933 · doi:10.1056/nejmoa2310234

Apixaban for Stroke Prevention in Subclinical Atrial Fibrillation

2023· article· en· W4388609933 on OpenAlexafffundabout
Jeff S. Healey, Renato D. Lópes, Christopher B. Granger, Marco Alings, Léna Rivard, William F. McIntyre, Dan Atar, David H. Birnie, Giuseppe Boriani, A. John Camm, David Conen, Julia W. Erath, Michael R. Gold, Stefan H. Hohnloser, John Ip, Josef Kautzner, Valentina Kutyifa, Cecilia Linde, Philippe Mabo, Georges H. Mairesse, Juan Benezet Mazuecos, Jens Cosedis Nielsen, François Philippon, Marco Proietti, Christian Sticherling, Jorge Wong, David J. Wright, Ignatius Zarraga, Shelagh B. Coutts, Andrew J. Kaplan, Marta Pombo, Félix Ayala-Paredes, Lizhen Xu, K Simek, Sandra Nevills, Rajibul Mian, Stuart J. Connolly

Bibliographic record

VenueNew England Journal of Medicine · 2023
Typearticle
Languageen
FieldMedicine
TopicAtrial Fibrillation Management and Outcomes
Canadian institutionsUniversity of CalgaryInstitut universitaire de cardiologie et de pneumologie de QuébecUniversité LavalPopulation Health Research InstituteUniversité de MontréalUniversité de SherbrookeMcMaster UniversityUniversity of OttawaMontreal Heart Institute
FundersCanadian Institutes of Health ResearchNovo Nordisk FondenCanadian Stroke NetworkInstitute of Circulatory and Respiratory HealthHamilton Health SciencesPfizerHeart and Stroke Foundation of CanadaMedtronicPopulation Health Research InstituteBristol-Myers SquibbBristol-Myers Squibb Foundation
KeywordsAtrial fibrillationApixabanSubclinical infectionStroke (engine)MedicineCardiologyInternal medicineWarfarinRivaroxaban

Abstract

fetched live from OpenAlex

BACKGROUND: Subclinical atrial fibrillation is short-lasting and asymptomatic and can usually be detected only by long-term continuous monitoring with pacemakers or defibrillators. Subclinical atrial fibrillation is associated with an increased risk of stroke by a factor of 2.5; however, treatment with oral anticoagulation is of uncertain benefit. METHODS: We conducted a trial involving patients with subclinical atrial fibrillation lasting 6 minutes to 24 hours. Patients were randomly assigned in a double-blind, double-dummy design to receive apixaban at a dose of 5 mg twice daily (2.5 mg twice daily when indicated) or aspirin at a dose of 81 mg daily. The trial medication was discontinued and anticoagulation started if subclinical atrial fibrillation lasting more than 24 hours or clinical atrial fibrillation developed. The primary efficacy outcome, stroke or systemic embolism, was assessed in the intention-to-treat population (all the patients who had undergone randomization); the primary safety outcome, major bleeding, was assessed in the on-treatment population (all the patients who had undergone randomization and received at least one dose of the assigned trial drug, with follow-up censored 5 days after permanent discontinuation of trial medication for any reason). RESULTS: -VASc score of 3.9±1.1 (scores range from 0 to 9, with higher scores indicating a higher risk of stroke); 36.1% of the patients were women. After a mean follow-up of 3.5±1.8 years, stroke or systemic embolism occurred in 55 patients in the apixaban group (0.78% per patient-year) and in 86 patients in the aspirin group (1.24% per patient-year) (hazard ratio, 0.63; 95% confidence interval [CI], 0.45 to 0.88; P = 0.007). In the on-treatment population, the rate of major bleeding was 1.71% per patient-year in the apixaban group and 0.94% per patient-year in the aspirin group (hazard ratio, 1.80; 95% CI, 1.26 to 2.57; P = 0.001). Fatal bleeding occurred in 5 patients in the apixaban group and 8 patients in the aspirin group. CONCLUSIONS: Among patients with subclinical atrial fibrillation, apixaban resulted in a lower risk of stroke or systemic embolism than aspirin but a higher risk of major bleeding. (Funded by the Canadian Institutes of Health Research and others; ARTESIA ClinicalTrials.gov number, NCT01938248.).

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)0.002
metaresearch head score (Gemma)0.002
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 score0.446
Threshold uncertainty score0.256

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
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 head0.126
GPT teacher head0.416
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 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

Citations415
Published2023
Admission routes3
Has abstractyes

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