Efficacy of adjunctive measures used to assist pulmonary vein isolation for atrial fibrillation
Bibliographic record
Abstract
PURPOSE OF REVIEW: Pulmonary vein reconnection leading to recurrence of atrial arrhythmias after pulmonary vein isolation (PVI) for atrial fibrillation remains a significant challenge. A number of adjunctive measures during PVI have been used to attempt to reduce pulmonary vein reconnection and recurrence of atrial arrhythmias. We performed a systematic review of the literature and meta-analysis of studies evaluating the efficacy of adjunctive measures used during PVI in reducing recurrent atrial arrhythmias. RECENT FINDINGS: Our literature search found four interventions that met the prespecified definition of adjunctive measure: adenosine testing post-PVI, contact force-guided PVI, pacing inexcitability of the ablation line during PVI and additional ablation based on the computed tomography thickness of the pulmonary vein-left atrial appendage ridge. Sixteen studies enrolling 3507 patients met all inclusion and exclusion criteria. PVI performed with adjunctive measures was shown to reduce the 1-year recurrence rate of atrial arrhythmias. The point estimate for the combined relative risk of atrial arrhythmia recurrence was 0.56 [95% confidence interval (CI): 0.43-0.73; P value <0.001] in the PVI with adjunctive measures group. SUMMARY: PVI for atrial fibrillation assisted by adjunctive measures results in clinically significant reduction of recurrent atrial arrhythmias. Additional research is required to assess the relative efficacy of individual or combined adjunctive strategies used during PVI for atrial fibrillation.
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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 | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".