Robotic-Assisted Cryosurgical Treatment of Atrioventricular Node Reentrant Tachycardia
Bibliographic record
Abstract
Atrioventricular nodal reentrant tachycardias typically arise from the existence of variable refractoriness in fast and slow conduction pathways within the triangle of Koch, which provide input to the atrioventricular node. Standard therapy includes medical management and catheter-based ablation procedures. Robotic-assisted, minimally invasive cryosurgical modification of the atrioventricular node can provide definitive therapy for patients who fail traditional therapy. A 65-year-old man presented with a several-year history of recurrent atrioventricular nodal reentrant tachycardia. Despite medical management and attempted percutaneous ablation, the patient remained symptomatic with weekly episodes. Access was via a 4-cm right anterolateral thoracotomy and peripheral perfusion. The da Vinci S robotic system was used to manipulate the cryoprobe (CryoMaze Probe; ATS Medical, Plymouth, MN USA). A series of spot freezes (tip 60°C) were made along the boundaries of the triangle of Koch until transient complete heart block was achieved and nodal rhythm was recovered. At follow-up 3 weeks postoperatively, the patient was asymptomatic in first-degree heart block. Robotic-assisted cryosurgical atrioventricular node ablation is an effective, minimally invasive treatment for patients with atrioventricular nodal reentrant tachycardia.
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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) | 0.001 | 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".