The role of high-intensity focused ultrasound in ablation of atrial fibrillation and other cardiac arrhythmias
Bibliographic record
Abstract
Asaf Danon,1 Krishna Kumar Mohanan Nair,2 Jacob S Koruth,3 Andre d’Avila,4 Sheldon M Singh5 1Department of Cardiology, Lady Davis Carmel Medical Center, Haifa, Israel; 2Department of Cardiology, Sree Chitra Tirunal Institute of Medical Sciences and Technology, Thiruvananthapuram, Kerala, India; 3Helmsley Electrophysiology Center, Mount Sinai School of Medicine, New York, NY, USA; 4Instituto de Pesquisa em Arritmia Cardiaca – Hospital Cardiologico, Florianopolis, SC, Brazil; 5Schulich Heart Program, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, ON, Canada Abstract: Atrial fibrillation is the most prevalent arrhythmia of the heart, originating usually from ectopic atrial activity of the pulmonary veins. Therefore, one of the treatment options is pulmonary vein isolation. Among the novel approaches to pulmonary vein isolation, high-intensity focused ultrasound (HIFU) has been developed. The ultrasound energy can be focused on a specific area and would result in the formation of a lesion similar to that formed with radiofrequency (RF) ablation. Although preclinical studies were promising, clinical studies in patients resulted in lower efficacy and high complication rates in comparison to the standard ablation method, due to collateral damage. Using HIFU for epicardial approach during cardiac surgery and for extracorporeal ablation does seem to have a future role. In this review, we present the mechanism of HIFU lesion formation and the principal studies. Keywords: atrial fibrillation, HIFU, high-intensity focused ultrasound, pulmonary vein isolation, epicardial, 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".