Utility of Cardiovascular Magnetic Resonance in Identifying Substrate for Malignant Ventricular Arrhythmias
Bibliographic record
Abstract
BACKGROUND: Sudden cardiac death (SCD) and sustained monomorphic ventricular tachycardia (SMVT) are frequently associated with prior or acute myocardial injury. Cardiovascular magnetic resonance (CMR) provides morphological, functional, and tissue characterization in a single setting. We sought to evaluate the diagnostic yield of CMR-based imaging versus non-CMR-based imaging in patients with resuscitated SCD or SMVT. METHODS AND RESULTS: Eighty-two patients with resuscitated SCD or SMVT underwent routine non-CMR imaging, followed by a CMR protocol with comprehensive tissue characterization. Clinical reports of non-CMR imaging studies were blindly adjudicated and used to assign each patient to 1 of 7 diagnostic categories. CMR imaging was blindly interpreted using a standardized algorithm used to assign a patient diagnosis category in a similar fashion. The diagnostic yield of CMR-based and non-CMR-based imaging, as well as the impact of the former on diagnosis reclassification, was established. Relevant myocardial disease was identified in 51% of patients using non-CMR-based imaging and in 74% using CMR-based imaging (P=0.002). Forty-one patients (50%) were reassigned to a new or alternate diagnosis using CMR-based imaging, including 15 (18%) with unsuspected acute myocardial injury. Twenty patients (24%) had no abnormality by non-CMR imaging but showed clinically relevant myocardial disease by CMR imaging. CONCLUSIONS: CMR-based imaging provides a robust diagnostic yield in patients presenting with resuscitated SCD or SMVT and incrementally identifies clinically unsuspected acute myocardial injury. When compared with non-CMR-based imaging, a new or alternate myocardial disease process may be identified in half of these patients.
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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 | 0.002 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".