Lower Incidence of Thrombus Formation With Cryoenergy Versus Radiofrequency Catheter Ablation
Bibliographic record
Abstract
BACKGROUND: Radiofrequency (RF) catheter ablation is limited by thromboembolic complications. The objective of this study was to compare the incidence and characteristics of thrombi complicating RF and cryoenergy ablation, a novel technology for the catheter-based treatment of arrhythmias. METHODS AND RESULTS: Ablation lesions (n=197) were performed in 22 mongrel dogs at right atrial, right ventricular, and left ventricular sites preselected by a randomized factorial design devised to compare RF ablation with cryocatheter configurations of varying sizes (7F and 9F), cooling rates (-1 degrees C/s, -5 degrees C/s, and -20 degrees C/s) and target temperatures (-55 degrees C and -75 degrees C). Animals were pretreated with acetylsalicylic acid and received intraprocedural intravenous unfractionated heparin. Seven days after ablation, the incidence of thrombus formation was significantly higher with RF than with cryoablation (75.8% versus 30.1%, P=0.0005). In a multiple regression model, RF energy remained an independent predictor of thrombus formation compared with cryoenergy (OR, 5.6; 95% CI, 1.7, 18.1; P=0.0042). Thrombus volume was also significantly greater with RF than with cryoablation (median, 2.8 versus 0.0 mm3; P<0.0001). More voluminous thrombi were associated with larger RF lesions, but cryolesion dimensions were not predictive of thrombus size. CONCLUSIONS: RF energy is significantly more thrombogenic than cryoenergy, with a higher incidence of thrombus formation and larger thrombus volumes. The extent of hyperthermic tissue injury is positively correlated with thrombus bulk, whereas cryoenergy lesion size does not predict thrombus volume, most likely reflecting intact tissue ultrastructure with endothelial cell preservation.
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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 | 0.002 |
| 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".