Deep Foci RF ablation using 3830 Medtronic pacemaker lead: proof of concept
Bibliographic record
Abstract
Introduction Radiofrequency (RF) is the preferred thermal energy used in electrophysiology. RF catheter must deliver the energy close to arrhythmia foci. A new method to deliver RF to deeper locations using a pacemaker lead is explored. Methods A Medtronic 3830 lead screwed in chicken breasts delivered 50 watts RF energy in three methods: A) direct fashion (RF catheter touching the proximal end of the 3830 lead, acting as an extension of RF catheter), or B) 3830 lead as a return patch (RF delivered in the bath without contact), or C) 3830 lead as a return patch (RF delivered touching the breast surface close to the 3830 lead screwed deep in the flesh). Different power settings were also tested. Lesion surface area is reported in cm2. Results 76 measurements were available. Bigger lesions were obtained at 10W method A (0.78cm2), 50W method C (0.72cm2) and 5W method B (0.44cm2). High impedances were noted at 10W and 50W with tissue remaining attached to the lead when removed. Conclusion RF can be delivered to deeper foci through a 3830-pacemaker lead with maximum size lesion formation using proximal unipolar direct delivery and proximal close bipolar as the return patch. In humans, it opens a path to attain deep septal foci (LV summit) or epicardial structures (vein of Marshall, transmural ablation from RF endocardial to LV coronary sinus lead as return patch): using standard, 4F pacemaker leads, and 2F small EP catheters or even isolated guidewires.
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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 | 0.000 | 0.000 |
| 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 | 0.001 |
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".