Novel case of linear ultra-low cryoablation catheter for treatment of ventricular tachycardia
Bibliographic record
Abstract
Key Teaching Points•Understand how new technologies can increase efficacy of ventricular tachycardia (VT) ablation. Novel technologies are focused on achieving the increased tissue depth required for VT ablation.•Lesion depth matters. Ultra-low-temperature cryoablation has the advantage of being able to titrate lesion depth according to tissue thickness in a given region.•In the near future, we will be able to choose between different technologies for VT ablation. Ultra-low-temperature cryoablation could be a useful ablation tool, creating deeper ventricular lesions compared to conventional radiofrequency. Studies assessing its safety and feasibility are ongoing. •Understand how new technologies can increase efficacy of ventricular tachycardia (VT) ablation. Novel technologies are focused on achieving the increased tissue depth required for VT ablation.•Lesion depth matters. Ultra-low-temperature cryoablation has the advantage of being able to titrate lesion depth according to tissue thickness in a given region.•In the near future, we will be able to choose between different technologies for VT ablation. Ultra-low-temperature cryoablation could be a useful ablation tool, creating deeper ventricular lesions compared to conventional radiofrequency. Studies assessing its safety and feasibility are ongoing.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.008 | 0.005 |
| 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".