Randomized Crossover Comparison of DDDR Versus VDD Pacing After Atrioventricular Junction Ablation for Prevention of Atrial Fibrillation
Bibliographic record
Abstract
BACKGROUND: Some clinical data suggest that atrial-based pacing prevents paroxysmal atrial fibrillation (AF). This study tested the hypothesis that DDDR pacing compared with VDD pacing prevents AF after atrioventricular (AV) junction ablation. METHODS AND RESULTS: Patients were randomized to DDDR pacing (n=33) or to VDD pacing (n=34) after AV junction ablation and followed every 2 months for 6 months. Patients then crossed over to the alternate pacing mode and were followed for an additional 6 months. Primary analysis included the time to first recurrence of sustained AF (duration >5 minutes), total AF burden, and the development of permanent AF. The time to first episode of AF was similar in the DDDR group (0.37 days, 95% CI 0.1 to 1.3 days) and the VDD pacing group (0.5 days, 95% CI 0.2 to 1.7 days, P=NS). AF burden increased over time in both groups (P<0.01). At the 6-month follow-up, AF burden was 6.93 h/d (95% CI 4. 37 to 10.96 h/d) in the DDDR group and 6.30 h/d (95% CI 3.99 to 9.94 h/d) in the VDD group (P=NS). Twelve (35%) patients in the DDDR group and 11 (32%) patients in the VDD group had permanent AF within 6 months of ablation. Within 1 year of follow-up, 43% of patients had permanent AF. CONCLUSIONS: DDDR pacing compared with VDD pacing does not prevent paroxysmal AF over the long term in patients in the absence of antiarrhythmic drug therapy after total AV junction ablation. Many patients have permanent AF within the first year after ablation.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".