Rate control in atrial fibrillation: looking beyond the average heart rate
Bibliographic record
Abstract
PURPOSE OF REVIEW: The aim of this article is to provide a perspective on rate control in atrial fibrillation which emphasizes patient wellbeing (exercise tolerance, symptoms, quality of life) over attempts to reduce resting or exercise heart rate to an arbitrary range. RECENT FINDINGS: Recent trials of rhythm versus rate control strategies of treatment in patients with atrial fibrillation suggest that rate control is a viable first line strategy in many patients. The adverse consequences of atrial fibrillation with rapid ventricular response are partly due to factors other than rate itself, such as irregularity of ventricular response, and variable changes in autonomic nervous system output. Digoxin, calcium channel blockers, and beta-blockers cause a similar reduction in resting heart rate. Beta blockers are the most potent at reducing exercise heart rate, followed by calcium channel blockers and digoxin. Exercise tolerance is occasionally improved by digoxin, sometimes improved by calcium channel blockers and not improved by (and sometimes decreased by) beta-blockers. Information about quality of life with different rate control regimens is sparse. SUMMARY: Rate control in atrial fibrillation provides important benefits to patients in terms of symptoms, quality of life and prevention of late consequences of uncontrolled rate (such as tachycardia induced cardiomyopathy). Restricting treatment objectives to achievement of a specific heart rate range on resting or exercise electrocardiogram may result in lack of patient benefit or worsened symptoms. Understanding the nuances of rate control when treating individual patients and interpreting existing evidence allows patients to experience the most benefit from this treatment strategy.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".