Remote Magnetic Navigation‐Assisted Catheter Ablation Enhances Catheter Stability and Ablation Success with Lower Catheter Temperatures
Bibliographic record
Abstract
BACKGROUND: It has been suggested that remote magnetic navigation (RMN) may provide enhanced catheter stability and substrate contact to aid in ablation. To date, no study has examined this claim. Accordingly, we compared the characteristics of the successful ablation of atrioventricular reentry tachycardia (AVNRT) using RMN with a matched population ablated using a conventional (CON) manual approach. METHODS: Sixteen patients who underwent RMN-assisted ablation of typical AVNRT were matched with 16 patients who had a CON-AVNRT ablation. RESULTS: All patients had successful slow pathway modification without complication. The mean catheter temperature achieved with the successful ablation was significantly lower with RMN than with CON (42 +/- 7 degrees C vs 47 +/- 3 degrees C, P <or= 0.05). Time to junctional tachycardia (JT) was significantly earlier (5.7 +/- 4.1 s vs 11.2 +/- 8.9 s, P <or= 0.05) and variation in catheter temperature with the successful ablation (0.89 +/- 0.45 vs 1.45 +/- 0.49, P < 0.01) was significantly reduced in the RMN group than in the CON group. There was no significant difference between RMN and CON in terms of the total number of lesions and the mean power achieved during the successful lesion. CONCLUSIONS: Although the construction of the ablation catheters is similar, ablations with RMN catheters resulted in a lower mean temperature, earlier time to JT, and less variability of temperature during ablation, suggesting greater catheter stability. This study indicates that ablation with RMN can achieve success with lower catheter temperatures.
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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 | 0.003 |
| 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".