The value of basic research insights into atrial fibrillation mechanisms as a guide to therapeutic innovation: a critical analysis
Bibliographic record
Abstract
Atrial fibrillation (AF) is an extremely common clinical problem associated with increased morbidity and mortality. Current antiarrhythmic options include pharmacological, ablation, and surgical therapies, and have significantly improved clinical outcomes. However, their efficacy remains suboptimal, and their use is limited by a variety of potentially serious adverse effects. There is a clear need for improved therapeutic options. Several decades of research have substantially expanded our understanding of the basic mechanisms of AF. Ectopic firing and re-entrant activity have been identified as the predominant mechanisms for arrhythmia initiation and maintenance. However, it has become clear that the clinical factors predisposing to AF and the cellular and molecular mechanisms involved are extremely complex. Moreover, all AF-promoting and maintaining mechanisms are dynamically regulated and subject to remodelling caused by both AF and cardiovascular disease. Accordingly, the initial presentation and clinical progression of AF patients are enormously heterogeneous. An understanding of arrhythmia mechanisms is widely assumed to be the basis of therapeutic innovation, but while this assumption seems self-evident, we are not aware of any papers that have critically examined the practical contributions of basic research into AF mechanisms to arrhythmia management. Here, we review recent insights into the basic mechanisms of AF, critically analyse the role of basic research insights in the development of presently used anti-AF therapeutic options and assess the potential value of contemporary experimental discoveries for future therapeutic innovation. Finally, we highlight some of the important challenges to the translation of basic science findings to clinical application.
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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 | 0.026 | 0.044 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.008 |
| Scholarly communication | 0.007 | 0.013 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.007 | 0.015 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".