Improved <i>In Vivo</i> Performance of Second‐Generation Cryoballoon for Pulmonary Vein Isolation
Bibliographic record
Abstract
INTRODUCTION: A novel cryoballoon with improved refrigerant distribution promises better pulmonary vein (PV) isolation success rate without sacrificing the technology's safety profile. This study aimed to compare the Arctic Front® (AF) balloon to the new Arctic Front Advance™ (AFA). METHODS AND RESULTS: Twenty pulmonary PVs from 10 healthy dogs weighing 29.8 ± 1.1 kg were assigned to ablation with AF and AFA, using a 23 mm or 28 mm balloon. A single 4-minute ablation was performed in each vessel, with no phrenic nerve monitoring. The Achieve™ mapping catheter was used to confirm acute isolation. Thirty days post-treatment the ablation sites were assessed for electrical PV isolation and ablation completeness via gross and histological examination. The phrenic nerve, PVs, lungs, esophagus, kidneys, and brain were collected for evaluation of potential damage. A preprocedural and prenecropsy CT were performed to assess incidence of PV stenosis. All 10 PVs were fully isolated with AFA; 6 of 10 PVs were fully isolated with AF. In all cases, lesion gaps with AF are believed to stem from inadequate cooling of the most distal balloon segment that was in contact with the unablated PV tissue. No untoward findings were detected on gross examination of the heart, esophagus, kidneys, brain, or PVs. One phrenic nerve had cross-sectional ablation associated with an AFA 23 mm balloon. Superficial regions of subpleural lung fibrosis were noted adjacent to 7 PVs. CONCLUSIONS: PV isolation and lesion completeness were improved with Arctic Front Advance, while no unexpected findings were found related to safety.
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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) | 0.000 | 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) | 0.001 | 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".