Best practices in robotic magnetic navigation-guided catheter ablation of cardiac arrhythmias, a position paper of the Society for Cardiac Robotic Navigation
Bibliographic record
Abstract
Preamble: Robotic magnetic navigation (RMN)-guided catheter ablation (CA) technology has been used for the treatment of cardiac arrhythmias for almost 20 years. Various studies reported that RMN allows for high catheter stability, improved lesion formation and a superior safety profile. So far, no guidelines or recommendations on RMN-guided CA have been published. Purpose: The aim of this consensus paper was to summarize knowledge and provide recommendations on management of arrhythmias using RMN-guided CA as treatment of atrial fibrillation (AF) and ventricular arrhythmias (VA). Methodology: An expert writing group, performed a detailed review of available literature, and drawing on their own experience, drafted and voted on recommendations and summarized current knowledge and practice in the field. Recommendations on RMN-guided CA are presented in a guideline format with three levels of recommendations to serve as a reference for best practices in RMN procedures. Each recommendation is accompanied by supportive text and references. The various sections cover the practical spectrum from system and patient set-up, EP laboratory staffing, combination of RMN with fluoroscopy and mapping systems, use of automation features and ablation settings and targets, for different cardiac arrhythmias. Conclusion: This manuscript, presenting the combined experience of expert robotic users and knowledge from the available literature, offers a unique resource for providers interested in the use of RMN in the treatment of cardiac arrhythmias.
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".