Combined pulsed field ablation with ultra‐low temperature cryoablation: A preclinical experience
Bibliographic record
Abstract
BACKGROUND: Combining pulsed field ablation (PFA) with ultra-low temperature cryoablation (ULTC) represents a novel energy source which may create more transmural cardiac lesions. We sought to assess the feasibility of lesions created by combined cryoablation and pulsed field ablation (PFCA) versus PFA alone. METHODS: Ablations were performed using a custom PFA generator, ULTC console, and an ablation catheter with insertable stylets. PFA was delivered in a biphasic, bipolar train. PFCA precooled the tissue for 30 s followed by a concurrent PFA train. Benchtop testing using Schlieren imaging and microbubble volume assessment were used to compare PFA and PFCA. PFA and PFCA lesions using pre-optimized and optimized ablation protocols were studied in 6 swine. Pre and post-ECGs were recorded for each ablation and a gross necropsy was performed at 14 days. RESULTS: Consistent with benchtop comparisons of heat and microbubble generation, PFA deliveries in the animals were accompanied by muscle contractions and significant microbubbles (Grade 2-3) visible on intracardiac echo while neither occurred during PFCA at higher voltage levels. Both PFA and PFCA acutely eliminated or highly attenuated (>80%) local atrial electrograms. Histology of PFA and PFCA lesions indicated depth up to 6-7 mm and nearly all lesions were transmural. Optimized PFCA produced wider cavotricuspid isthmus lesions with evidence of tissue selectivity. CONCLUSION: A novel technology combining PFA and ULTC into one energy source demonstrated in-vivo feasibility for PFCA ablation. PFCA had a more favorable thermal profile and did not produce muscle contraction or microbubbles while extending lesion depth beyond cryoablation.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".