Overview of Cardiac Arrhythmias and Treatment Strategies
Bibliographic record
Abstract
Maintenance of normal cardiac rhythm requires coordinated activity of ion channels and transporters that allow well-ordered propagation of electrical impulses across the myocardium. Disruptions in this orderly process provoke cardiac arrhythmias that may be lethal in some patients. Risk of common acquired arrhythmias is increased markedly when structural heart disease caused by myocardial infarction (due to fibrotic scar formation) or left ventricular dysfunction is present. Genetic polymorphisms influence structure or excitability of the myocardial substrate, which increases vulnerability or risk of arrhythmias in patients. Similarly, genetic polymorphisms of drug-metabolizing enzymes give rise to distinct subgroups within the population that affect specific drug biotransformation reactions. Nonetheless, identification of triggers involved in initiation or maintenance of cardiac arrhythmias remains a major challenge. Herein, we provide an overview of knowledge regarding physiopathology of inherited and acquired cardiac arrhythmias along with a summary of treatments (pharmacologic or non-pharmacologic) used to limit their effect on morbidity and potential mortality. Improved understanding of molecular and cellular aspects of arrhythmogenesis and more epidemiologic studies (for a more accurate portrait of incidence and prevalence) are crucial for development of novel treatments and for management of cardiac arrhythmias and their consequences in patients, as their incidence is increasing worldwide.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".