Modifiable Risk Factors and Atrial Fibrillation
Bibliographic record
Abstract
There has been increasing focus on the rising burden of atrial fibrillation (AF) since the turn of the millennium. The AF epidemic is projected not only to have an impact on morbidity and mortality, but also to result in increasing healthcare use and cost. Intensive research over the previous decades has improved our understanding of this complex arrhythmia while unraveling more knowledge gaps and inadequacies of current therapeutic options. Specifically, the advances in catheter ablation technology and strategies have not translated into significant gains in procedural success rates over recent years. Therefore, strategies aiming at lowering the risk of AF development and progression are urgently needed to curtail the AF epidemic and improve outcomes in affected individuals. Recent research has highlighted the potential beneficial effects of lifestyle and risk factor management for AF as upstream noninvasive therapy. The evidence supporting this treatment paradigm beyond routine clinical AF management argues for change in the delivery of care to patients who have this debilitating arrhythmia. In this review, we highlight the contributory role of risk factors to AF pathogenesis from both bench and bedside studies. Next, we discuss the rationale and potential benefits of risk factor modification for sinus rhythm maintenance. Last, we propose an integrated care model to incorporate risk factor modification as the fourth pillar of AF care in conjunction with established pillars of rate control, rhythm control, and anticoagulation therapy.
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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 | 0.002 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".