Recent Advances in Antiarrhythmic Drug Therapy
Bibliographic record
Abstract
Cardiac arrhythmias remain a common cause of death and disability. Antiarrhythmic drugs (AADs) and antiarrhythmic agents remain a cornerstone of current cardiac arrhythmia management, despite moderate efficacy and the potential for significant adverse proarrhythmic effects. Due to conceptual, regulatory and financial considerations, the number of novel antiarrhythmic targets and agents in the development pipeline has decreased substantially during the last few decades. However, several promising candidates remain and there are exciting developments in repurposing and reformulating already existing drugs for indications related to cardiac arrhythmias. This review discusses the key conceptual considerations for the development of new antiarrhythmic agents, summarizes new compounds and formulations currently in clinical development for rhythm control of atrial fibrillation, and highlights the potential for drug repurposing. Finally, future directions in AAD development are discussed. Together with an ever-increasing understanding of the molecular mechanisms underlying cardiac arrhythmias, these components support a cautiously optimistic outlook towards improved pharmacological treatment opportunities for patients suffering from cardiac arrhythmias.
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 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 | 0.001 |
| 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) | 0.009 | 0.005 |
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".