Characteristics of pulsed electric field cardiac ablation porcine treatment zones with a focal catheter
Bibliographic record
Abstract
OBJECTIVES: Pulsed electric field (PEF) therapies employ punctuated energy delivery to kill cells in a volume of tissue through mechanisms that are not dependent on thermal processes. A key component to successful cardiac ablation procedures is ensuring the generation of transmural, contiguous ablation zones, which requires in-depth knowledge regarding treatment sizes for a given therapeutic application. METHODS: In this study, a series of acute treatments were delivered to porcine ventricles, where triphenyl tetrazolium chloride (TTC) vitality stain was used to identify treatment effect sizes for the three focal monopolar CENTAURI PEF cardiac ablation energy settings. RESULTS: Treatment depths were 5.7, 7.2, and 8.2 mm for the 19, 22, and 25 A energy settings, respectively. Gross pathology indicated umbral zones of hemorrhage surrounded by pale avital TTC-negative-negative tissue, which contrasted significantly from radiofrequency ablation (RF) controls. Histologically, treatment zones are identified by regions of contraction band necrosis and cardiomyocytolysis, which contrasted with RF control lesions composed primarily of coagulation necrosis. CONCLUSIONS: Together, these data indicate the ability for focal monopolar PEF treatments to generate deep treatment zones in cardiac ablation without incurring the gross or histological coagulative characteristics of RF thermal lesions.
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".