Landing on the spot: Approaches to outflow tract PVCs; from ECG to EGMs to intracardiac echocardiography
Bibliographic record
Abstract
Premature ventricular complexes (PVCs) are increasingly recognized, as the use of ECG wearables becomes more widespread. In particular, PVCs arising from both the right ventricular outflow tract (RVOT) and left ventricular outflow tract (LVOT) comprise the majority of these arrhythmias and form a significant component of an electrophysiology practice. A keen understanding of the correlative anatomy of the outflow tracts, in addition to recognizing key ECG indices illustrating PVC sites of origin, are fundamental in preparing for a successful ablation. Patient selection, incorporating symptomatology, structural disease, and PVC burden can pose a challenge, though tools such as the ABC-VT risk score may help identify those patients with a higher risk of clinical deterioration. Utilizing intracardiac echocardiography to highlight salient anatomic features not visible with fluoroscopy allows for a more precise and safer ablation. Interpretation of intracardiac EGMs, and the careful examination for low amplitude highly fractionated pre-potentials, enhanced by the advent of new developed mapping/ablation catheters, remains crucial. Utilizing these tools will guide the electrophysiologist to an efficient and effective outflow tract PVC ablation.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".