Selective CFAE Targeting for Atrial Fibrillation Study (SELECT AF): Clinical Rationale, Design, and Implementation
Bibliographic record
Abstract
BACKGROUND: Adjuvant ablation of complex fractionated atrial electrograms (CFAE) in addition to pulmonary vein isolation (PVI) likely improves procedural outcome compared to PVI alone, particularly in patients with persistent atrial fibrillation (AF). However, CFAE regions can be extensive, occasionally requiring a large amount of extra ablation. Some CFAE regions may also represent passive wavefront collision and may not require ablation. Thus, there is interest in identifying more selective CFAE sites that are critical to AF perpetuation, minimizing the amount of adjuvant ablation that must be performed. OBJECTIVE: The SELECT AF study is a prospective, multicenter, randomized trial comparing a strategy of PVI plus generalized CFAE ablation versus a strategy of PVI plus selective CFAE ablation, focusing on regions of continuous electrical activity (CEA). The primary efficacy endpoint is freedom from atrial arrhythmia at 1 year and the primary safety endpoint is total radiofrequency (RF) delivery time per procedure. METHODS: Patients undergoing a first time ablation procedure for symptomatic persistent AF will be included. Patients with permanent AF or with left atrial size ≥55 mm will be excluded. Patients will all receive PVI at the time of their ablation, but will be randomized 1:1 to receive adjuvant CFAE ablation using the traditional "generalized" approach, or a "selective" approach targeting only CEA regions. Both strategies will be guided by automated mapping algorithms. This study will enroll a minimum of 80 evaluable subjects; 40 in each randomization group. CONCLUSIONS: SELECT AF is a randomized trial in patients with persistent AF to evaluate the efficacy of selective versus generalized CFAE ablation in addition to traditional PVI.
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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 | 0.018 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".