Atrial fibrillation for internists: current practice
Bibliographic record
Abstract
Atrial fibrillation (AF) has become a global epidemic and puts affected patients at high risk of adverse events. In this review we summarise the current evidence on risk factors and complications of AF, describe current treatment strategies, and outline new fields of research. Current evidence shows that hypertension and obesity are the two most important modifiable risk factors for the development of AF. Patients with AF face an increased stroke risk. Oral anticoagulation reduces this risk substantially. Mainly for reasons of safety and ease of use, non-vitamin K antagonist oral anticoagulants are preferred for stroke prevention. Rate and rhythm control interventions remain important and are mainly used for symptom control in AF patients. Rate control is recommended as an initial treatment and in patients with a low or absent symptom burden. Following the advent of AF ablation 20 years ago, the chances of successful sustained rhythm control have increased. Nevertheless, the procedural risks, although low, must be discussed with the patient in the context of the potential benefits. Heart failure and AF often coexist, which creates a further challenge for optimal AF management. Recent studies have shown that AF patients have a high burden of silent brain lesions, and that these lesions are associated with cognitive dysfunction. A better understanding of these interrelationships may eventually help the development of new prevention and treatment strategies to decrease the burden and complications associated with AF.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.035 | 0.015 |
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".