Pulse Field Energy (of Dreams): Is it the Future of Ablation?
Bibliographic record
Abstract
Catheter ablation is the cornerstone of management of cardiac arrhythmias. Thermal energy sources such as radiofrequency energy and cryoenergy have proven to be effective and safe for ablating myocardial tissue responsible for the initiation and maintenance of cardiac arrhythmias. However, thermal energy causes significant collateral damage because of ablation-related coagulation necrosis and inflammation. Phrenic nerve damage, pulmonary vein stenosis, and esophageal mucosal injury that can result in atrio-esophageal fistula are some of the most concerning complications associated with thermal ablation. Pulsed field energy is a novel energy source that can cause selective, nonreversible electroporation of cardiac myocytes, resulting in apoptotic cell death. Early preclinical and clinical studies indicate that pulsed field ablation (PFA) is as effective as thermal energy for ablation of atrial fibrillation. The tissue-specific nature of this energy source causes minimal collateral damage, resulting in a favorable safety profile. PFA is also being evaluated for the ablation of non-atrial fibrillation supraventricular arrhythmias and ventricular arrhythmias. This review summarizes the rapid development of a wide array of PFA systems and its developing role as the primary modality for catheter ablation. PFA offers the promise of tissue- and patient-specific ablation solutions for cardiac arrhythmias.
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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 | 0.009 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.015 | 0.003 |
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".