Durability of Ultra‐Low Temperature Cryoablation Lesions in Atrial Fibrillation: Insights From Repeat Ablation Procedures
Bibliographic record
Abstract
BACKGROUND: Ultra-low temperature cryoablation (ULTC) is a technique designed to rapidly cool cardiac tissue to extremely low temperatures, enabling the creation of ablation lesions for the treatment of atrial fibrillation (AF). Prior studies have demonstrated low rates of arrhythmia recurrence, but little is known about ablation lesion durability. METHODS: Patients undergoing repeat ablation were selected from the CryoCure2 (NCT02839304) and iCLAS PMCF(NCT05416086) studies. Baseline patient and ULTC procedure characteristics were evaluated. During repeat ablation, ULTC ablation lesions were assessed for electrical block, including segment-based assessment of pulmonary vein (PV) ablation lesions. Arrhythmia outcomes after repeat ablation were evaluated. RESULTS: . During index procedure, ULTC was used to target the PVs in all patients, the left atrium posterior wall (LAPW) in 15 patients, the lateral mitral isthmus (LMI) in five patients and the cavotricuspid isthmus (CTI) in two patients. At repeat ablation, PV reconnection was observed in 21/25 patients (55/100 PVs reconnected), and reconnection occurred most often in the anterior segments of the left PVs. The LAPW lesion was incomplete in 4/15 patients, the LMI in 3/5 and the CTI in 1/2. After repeat ablation, 10/25 patients had arrhythmia recurrence. CONCLUSION: Reconnection of ablation targets during repeat ablation for arrhythmia recurrence following ULTC occurred at rates comparable to those observed with conventional thermal ablation modalities. The anterior side of the left PVs appears to be reconnected most often.
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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 | 0.005 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".