Patient selection, ventricular tachycardia substrate delineation, and data transfer for stereotactic arrhythmia radioablation: a clinical consensus statement of the European Heart Rhythm Association of the European Society of Cardiology and the Heart Rhythm Society
Bibliographic record
Abstract
Stereotactic arrhythmia radioablation (STAR) is a novel, non-invasive, and promising treatment option for ventricular arrhythmias (VAs). It has been applied in highly selected patients mainly as bailout procedure, when (multiple) catheter ablations, together with anti-arrhythmic drugs, were unable to control the VAs. Despite the increasing clinical use, there is still limited knowledge of the acute and long-term response of normal and diseased myocardium to STAR. Acute toxicity appeared to be reasonably low, but potential late adverse effects may be underreported. Among published studies, the provided methodological information is often limited, and patient selection, target volume definition, methods for determination and transfer of target volume, and techniques for treatment planning and execution differ across studies, hampering the pooling of data and comparison across studies. In addition, STAR requires close and new collaboration between clinical electrophysiologists and radiation oncologists, which is facilitated by shared knowledge in each collaborator's area of expertise and a common language. This clinical consensus statement provides uniform definition of cardiac target volumes. It aims to provide advice in patient selection for STAR including aetiology-specific aspects and advice in optimal cardiac target volume identification based on available evidence. Safety concerns and the advice for acute and long-term monitoring including the importance of standardized reporting and follow-up are covered by this document. Areas of uncertainty are listed, which require high-quality, reliable pre-clinical and clinical evidence before the expansion of STAR beyond clinical scenarios in which proven therapies are ineffective or unavailable.
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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 | 0.061 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.006 | 0.004 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.002 | 0.003 |
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".