Deciphering the fundamental mechanisms of atrial fibrillation: a quest for over a century
Bibliographic record
Abstract
Atrial fibrillation (AF), the most frequent clinical arrhythmia, is associated with increased cardiovascular morbidity and mortality, with stroke, myocardial infarction, and heart failure being the most critical complications.1 Presently available drugs for AF therapy have moderate efficacy and important limitations, particularly increasing the risk of life-threatening proarrhythmic events and bleeding complications.1,2 Ablation procedures are moderately effective and relatively safe, but the increasing size of the patient population limits applicability to only a small proportion of patients.1 Therefore, drug therapy is still the mainstay of AF treatment. Although maintenance of sinus rhythm (rhythm control) appears preferable, clinical studies failed to demonstrate clear advantages to rate over rhythm control, likely because current pharmacological approaches do not target the critical determinants of the fundamental mechanisms of AF.3,4 A better mechanistic understanding of the molecular basis of AF is expected to foster the development of safer and more effective treatment approaches. Although the basic mechanisms of AF have been described in the medical literature for over a century, the underlying cellular and molecular mechanisms are incompletely understood.5 It is assumed that independent of the underlying cause, which may be very diverse, ectopic impulse formation (ectopic activity) and re-entry are the two major determinants of AF pathophysiology.2 Re-entry requires a vulnerable substrate and an initiating trigger. The likelihood of re-entry formation is determined by the tissue properties of conduction and refractoriness, with conduction disturbances and short refractoriness making formation …
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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 teacher head, 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".