Geometric Features of Ventricular Tachycardia Corridors in Patients With Ischemic Cardiomyopathy
Bibliographic record
Abstract
BACKGROUND: A 3-dimensional hyperboloid model has been proposed to characterize ventricular tachycardia (VT) circuitry. We sought to characterize the geometric features of viable corridors, derived from late gadolinium enhanced cardiovascular magnetic resonance, that participate in VT circuitry in ischemic cardiomyopathy. METHODS: In this retrospective cohort study, we analyzed patients with ischemic cardiomyopathy who underwent cardiovascular magnetic resonance before their first VT ablation between November 2018 and May 2024. Viable corridors traversing infarct tissue on late gadolinium enhanced images were coregistered with VT corridor entrance and exit site coordinates, identified by entrainment/pace mapping. The prevalence of VT corridor geometry (hyperboloid, funnel, or cylinder) and corridor ostium angle, width, length, thickness, volume, and accessibility from chambers accessed during the procedure were measured. RESULTS: The cohort included 46 patients (95.4% male; 68±8 years of age). Of 125 VT exit sites that registered to a corridor ostium among 45 patients, central corridors predominantly exhibited hyperbola geometry (93.6%), with 4.8% exhibiting funnel and 1.6% exhibiting cylinder geometry. Of 11 VT entrance sites that registered to a corridor ostium among 5 patients, all central corridors exhibited hyperbola geometry. The mean angle of corridor ostium at VT exit was significantly larger than the angle of the opposite ostium (mean±SD, 102.8°±34.1° versus 83.2°±29.5°; P < 0.001). CONCLUSIONS: Most VT corridors in patients with ischemic cardiomyopathy, derived from late gadolinium enhanced magnetic resonance images and validated by mapping, were hyperboloid, but funnel and cylinder shapes were also seen. The central hyperbola exhibits a larger opening angle at the exit ostium than the entrance.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".