Effects of Electrode-Tissue Proximity on Cardiac Lesion Formation Using Pulsed Field Ablation
Bibliographic record
Abstract
BACKGROUND: Pulsed field ablation (PFA) is a novel energy modality for treatment of cardiac arrhythmias. The impact of electrode-tissue proximity on lesion formation by PFA has not been conclusively assessed. The objective of this investigation was to evaluate the effects of electrode-tissue proximity on cardiac lesion formation with a biphasic, bipolar PFA system. METHODS: PFA was delivered on the ventricular epicardial surface in an isolated porcine heart model (n=8) via a 4-electrode prototype catheter. An offset tool was designed to control the distance between electrodes and target tissue; deliveries were placed 0 mm (0 mm offset), 2 mm (2 mm offset), and 4 mm away from the tissue (4 mm offset). Lesions were assessed using tetrazolium chloride staining. Numerical models for the experimental setup with and without the offset tool validated and supported results. RESULTS: Cardiac lesion dimensions decreased proportional to the distance between epicardial surface and electrodes. Lesion depth averaged 4.3±0.4 mm, 2.7±0.4 mm, and 1.3±0.4 mm for the 0, 2, and 4 mm and lesion width averaged 9.4±1.1 mm, 7.5±0.8 mm and 5.8±1.4 mm for the 0, 2, and 4 mm offset distances, respectively. Numerical modeling matched ex vivo results well and predicted lesion creation with and without the offset tool. CONCLUSIONS: Using a biphasic, bipolar PFA system resulted in cardiac lesions even in the 0 mm offset distance case. The relationship between lesion depth and offset distance was linear, and the deepest lesions were created with 0 mm offset distance, that is, with electrodes in contact with tissue. Therefore, close electrode-tissue proximity increases the likelihood of achieving transmural lesions by maximizing the electric field penetration into the target tissue.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".