The Clinical Profile and Pathophysiology of Atrial Fibrillation
Bibliographic record
Abstract
Atrial fibrillation (AF) is the most common arrhythmia (estimated lifetime risk, 22%-26%). The aim of this article is to review the clinical epidemiological features of AF and to relate them to underlying mechanisms. Long-established risk factors for AF include aging, male sex, hypertension, valve disease, left ventricular dysfunction, obesity, and alcohol consumption. Emerging risk factors include prehypertension, increased pulse pressure, obstructive sleep apnea, high-level physical training, diastolic dysfunction, predisposing gene variants, hypertrophic cardiomyopathy, and congenital heart disease. Potential risk factors are coronary artery disease, kidney disease, systemic inflammation, pericardial fat, and tobacco use. AF has substantial population health consequences, including impaired quality of life, increased hospitalization rates, stroke occurrence, and increased medical costs. The pathophysiology of AF centers around 4 general types of disturbances that promote ectopic firing and reentrant mechanisms, and include the following: (1) ion channel dysfunction, (2) Ca(2+)-handling abnormalities, (3) structural remodeling, and (4) autonomic neural dysregulation. Aging, hypertension, valve disease, heart failure, myocardial infarction, obesity, smoking, diabetes mellitus, thyroid dysfunction, and endurance exercise training all cause structural remodeling. Heart failure and prior atrial infarction also cause Ca(2+)-handling abnormalities that lead to focal ectopic firing via delayed afterdepolarizations/triggered activity. Neural dysregulation is central to atrial arrhythmogenesis associated with endurance exercise training and occlusive coronary artery disease. Monogenic causes of AF typically promote the arrhythmia via ion channel dysfunction, but the mechanisms of the more common polygenic risk factors are still poorly understood and under intense investigation. Better recognition of the clinical epidemiology of AF, as well as an improved appreciation of the underlying mechanisms, is needed to develop improved methods for AF prevention and management.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".