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Record W4416202270 · doi:10.1016/j.jacep.2025.09.014

Atrial Fibrillation and Heart Failure With Reduced Ejection Fraction

2025· article· en· W4416202270 on OpenAlexafffund
Andreas A. Boehmer, Joachim R. Ehrlich, Stanley Nattel

Bibliographic record

VenueJACC. Clinical electrophysiology · 2025
Typearticle
Languageen
FieldMedicine
TopicAtrial Fibrillation Management and Outcomes
Canadian institutionsMontreal Heart Institute
FundersCanadian Institutes of Health Research
KeywordsAtrial fibrillationHeart failureEjection fractionCatheter ablationNarrative reviewHeart RhythmQuality of life (healthcare)

Abstract

fetched live from OpenAlex

Atrial fibrillation (AF) and heart failure with reduced ejection fraction (HFrEF) frequently occur together as a result of shared functional, structural, and electrical remodeling, thereby worsening the prognosis and quality of life of affected patients. Despite substantial advances in elucidating the complex interplay between AF and HFrEF, numerous critical questions remain unanswered, underscoring the need for continued investigation to address the persistent uncertainties surrounding this challenging comorbidity. The development and wide application of catheter ablation for rhythm control has highlighted the importance of controlling AF in patients with HFrEF, along with the benefits of rhythm control in this population. Whereas early rhythm control appears to be important, many questions remain unanswered about optimal timing parameters, benefits of specific energy sources and lesion sets, and the role of ancillary pharmacologic therapy. Personalized management is essential for optimized care of patients with AF and HFrEF, but extensive additional research is required to enable the individualized care needed. In this narrative review, we aim to analyze the rapidly evolving evidence regarding the interrelationships between these common conditions, discuss the underlying basic mechanisms, review evidence regarding treatment options, and highlight gaps in knowledge.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.147
Threshold uncertainty score0.420

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.036
GPT teacher head0.381
Teacher spread0.345 · 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

Citations3
Published2025
Admission routes2
Has abstractyes

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