Atrial Fibrillation and Heart Failure With Reduced Ejection Fraction
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".