Approach to the Catheter Ablation Technique of Paroxysmal and Persistent Atrial Fibrillation: A Meta-Analysis of the Randomized Controlled Trials
Bibliographic record
Abstract
BACKGROUND: Several randomized controlled trials (RCTs) have been published to investigate the optimal techniques for atrial fibrillation (AF) ablation. Many of these are small in number and include both paroxysmal and persistent AF; however, the techniques for each of these types of AF may differ. METHOD AND RESULTS: We searched MEDLINE, EMBASE, and the Cochrane Controlled Trials Register for RCTs evaluating AF ablation for either paroxysmal or persistent AF. The primary endpoint was freedom from AF after a single procedure. A total of 35 unique randomized controlled trials were found to fulfill the criteria. A significant degree of heterogeneity was present given the differing sample sizes, populations studied, and outcomes. Radiofrequency ablation (RFA) was found to be favorable in prevention of AF over antiarrhythmic drugs (AADs) in either paroxysmal (5 studies, RR 2.26; 95% CI 1.74, 2.94) or persistent AF (5 studies, RR 3.20; 95% CI 1.29, 8.41). When comparing specific techniques, wide-area PVI appeared to offer the most benefit for both paroxysmal (6 studies, RR 0.78; 95% CI 0.63, 0.97) and persistent AF (3 studies, RR 0.64; 95% CI 0.43, 0.94). CFE ablation provided only benefit for persistent AF when combined with antral PVI (4 studies, RR 0.55; 95% CI 0.34, 0.87). CONCLUSIONS: Despite significant methodological limitations, it appears that additional ablations beyond PVI are necessary for persistent AF but not proven for paroxysmal AF. The optimal technique for persistent AF, however, deserves a further study, in the setting of a large, randomized controlled trial.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.064 | 0.118 |
| Meta-epidemiology (narrow) | 0.005 | 0.002 |
| Meta-epidemiology (broad) | 0.034 | 0.086 |
| Bibliometrics | 0.014 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".