Right ventricular outflow tract low‐voltage areas identify the site of origin of idiopathic ventricular arrhythmias: A high‐density mapping study
Bibliographic record
Abstract
Abstract Introduction Electronatomical mapping allows direct and accurate visualization of myocardial abnormalities. This study investigated whether high‐density endocardial bipolar voltage mapping of the right ventricular outflow tract (RVOT) during sinus rhythm may guide catheter ablation of idiopathic ventricular arrhythmias (VAs). Methods and Results Forty‐four patients (18 males, mean age: 38.1 ± 13.8 years) with idiopathic RVOT VAs and negative cardiac magnetic resonance imaging underwent a stepwise mapping approach for the identification of the site of origin (SOO). High‐density electronatomical mapping (1096.6 ± 322.3 points) was performed during sinus rhythm and identified at least two low bipolar voltage areas less than 1 mV (mean amplitude of 0.20 ± 0.10 mV) in 39 of 44 patients. The mean low‐voltage surface area was 1.4 ± 0.8 cm2. Group 1 consisted of 28 patients exhibiting low‐voltage areas and high‐arrhythmia burden during the procedure. Pace match to the clinical VAs was produced in one of these low‐voltage areas. Activation mapping established the SOO at these sites in 27 of 28 cases. Group 2 comprised 11 patients exhibiting abnormal electroanatomical mapping, but very low‐arrhythmia burden during the procedure. Pace mapping produced a near‐perfect or perfect match to the clinical VAs in one of these areas in 9 of 11 patients which was marked as potential SOO and targeted for ablation. During the follow‐up period, 25 of 28 patients from group 1 (89%) and 7 of 9 patients from group 2 (78%) were free from VAs. Conclusions Small but detectable very low‐voltage areas during mapping in sinus rhythm characterize the arrhythmogenic substrate of idiopathic RVOT VAs and may guide successful catheter ablation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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".