Unconventional warfare: Successful ablation of ventricular tachycardia by direct ventricular puncture in a patient with double mechanical heart valves
Bibliographic record
Abstract
Key Teaching Points•Catheter ablation of ventricular tachycardia (VT) can improve morbidity and mortality in patients with structural heart disease with implantable cardioverter-defibrillators implanted for primary as well as secondary prevention.•Substrate-based ablation is a safe and effective strategy for reentrant VTs, which is the predominant mechanism of tachycardia in these patients.•Access to the “substrate” will be challenging in some cases, especially in the presence of mechanical prosthetic heart valves.•Unconventional approaches are needed in those cases where a “hybrid” approach of catheter-based ablation and surgical ablation is useful. •Catheter ablation of ventricular tachycardia (VT) can improve morbidity and mortality in patients with structural heart disease with implantable cardioverter-defibrillators implanted for primary as well as secondary prevention.•Substrate-based ablation is a safe and effective strategy for reentrant VTs, which is the predominant mechanism of tachycardia in these patients.•Access to the “substrate” will be challenging in some cases, especially in the presence of mechanical prosthetic heart valves.•Unconventional approaches are needed in those cases where a “hybrid” approach of catheter-based ablation and surgical ablation is useful.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.004 |
| 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".