Right‐Side RF Ablation Using Remote Catheter Navigation: Experimental Results <i>In Vivo</i>
Bibliographic record
Abstract
INTRODUCTION: The close proximity between the interventionalist and patient during catheter-based interventions for cardiac arrhythmia exposes the interventionalist to harmful radiation. A prototype remote catheter navigation system (RCNS) has been developed to reduce occupational dose. The safety, feasibility of this RCNS and a comparison of remote and conventional navigation techniques is investigated in vivo. METHODS: Seven anatomical locations in the right side of the heart in porcine models were chosen as navigation targets. Using fluoroscopy and electrogram analysis, an experienced electrophysiology interventionalist manipulated a radiofrequency (RF) ablation catheter to each target using the RCNS and conventional navigation. Success rate, navigation time, exposure, exposure time and procedure time was recorded for all anatomical targets. Time to integrate the RCNS with the procedure suite was also measured. RESULTS: All targets were successfully reached with the RCNS and conventional navigation. No erratic catheter motion was observed with the RCNS whereas 1 operation failure occurred. The anatomical targets were found to have the largest effect on navigation time (P < 0.05), exposure (P < 0.05), and exposure time (P < 0.01), although the navigation method had little to no effect on the metrics. These results suggest that remote navigation procedures can be performed with navigation times comparable to conventional bedside navigation. CONCLUSION: Remote navigation with the RCNS may present a safe method of reducing occupational dose, while providing comparable navigation time with conventional bedside navigation.
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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 | 0.001 |
| 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) | 0.002 | 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".