Reduction in Pulmonary Vein Stenosis and Collateral Damage With Pulsed Field Ablation Compared With Radiofrequency Ablation in a Canine Model
Bibliographic record
Abstract
BACKGROUND: Pulmonary vein (PV) stenosis is a highly morbid condition that can result after catheter ablation for PV isolation. We hypothesized that pulsed field ablation (PFA) would reduce PV stenosis risk and collateral injury compared with irrigated radiofrequency ablation (IRF). METHODS: IRF and PFA deliveries were randomized in 8 dogs with 2 superior PVs ablated using one technology and 2 inferior PVs ablated using the other technology. IRF energy (25-30 W) or PFA was delivered (16 pulse trains) at each PV in a proximal and in a distal site. Contrast computed tomography scans were collected at 0, 2, 4, 8, and 12-week (termination) time points to monitor PV cross-sectional area at each PV ablation site. RESULTS: ≤0.023). Necropsy showed expansive PFA lesions without stenosis in the proximal PV sites, compared with more confined and often incomplete lesions after IRF. At the distal PV sites, only IRF ablations were grossly identified based on focal fibrosis. Mild chronic parenchymal hemorrhage was noted in 3 left superior PV lobes after IRF. Damage to vagus nerves as well as evidence of esophagus dilation occurred at sites associated with IRF. In contrast, no lung, vagal nerve, or esophageal injury was observed at PFA sites. CONCLUSIONS: PFA significantly reduced risk of PV stenosis compared with IRF postprocedure in a canine model. IRF also caused vagus nerve, esophageal, and lung injury while PFA did not.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".