Pacing delivered rate and rhythm control for atrial fibrillation
Bibliographic record
Abstract
PURPOSE OF REVIEW: Device therapy for atrial fibrillation remains contentious despite the recognized benefit of atrial pacing in sinus node dysfunction. There are various new specialized pacing algorithms that aim to provide rhythm or rate control in atrial fibrillation. We review the various options for device therapy and the evidence available concerning their effectiveness. RECENT FINDINGS: Randomized trials on preventative algorithms for atrial fibrillation have not shown consistent benefit. Anti-tachycardia pacing for atrial fibrillation has inherent problems illustrated in this review and has failed to demonstrate objective improvement except in the case of atrial flutter. Several large randomized trials have demonstrated an adverse outcome with right ventricular apical pacing. These studies have shown an increase in atrial fibrillation with ventricular pacing. Recent studies have emphasised the importance of right ventricular apical pacing in burden of atrial fibrillation and therefore we discuss the likely confounding effect on previous trials and speculate on future directions. SUMMARY: The use of a device with atrial fibrillation prevention algorithms in a patient with a bradycardia indication for pacing is not unreasonable but there is no hard evidence of benefit. Patients with sinus node dysfunction should be paced in the atrium alone. There is no indication for use of a device for atrial fibrillation without a conventional indication for pacing.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".