Virtual electrophysiological study as a tool for evaluating efficacy of MRI techniques in predicting adverse arrhythmic events in ischemic patients
Bibliographic record
Abstract
Myocardial infarct (MI) related indices determined by late gadolinium enhancement (LGE) MRI have been widely investigated in determining patients suitable for implantable cardiovascular-defibrillator (ICD) therapy to complement left ventricular ejection fraction (LV EF). In comparison to LGE-MRI using inversion-recovery fast-gradient-echo (IR-FGRE), T1 mapping techniques, such as multi contrast late enhancement (MCLE), have been shown to provide more quantitative and reproducible estimates of infarct regions. The objective of this study is to use individualized heart computer models in determining the efficacy of IR-FGRE and MCLE techniques in predicting the occurrence of post-MI ventricular tachycardia (VT). Twenty-seven patients with MI underwent LGE-MRI using IR-FGRE and MCLE prior to ICD implantation and were followed up for 6-46 months. Individualized image-based computational models were built separately for each imaging technique; simulations of propensity to VT were conducted with each model. The imaging methods were evaluated by comparing simulated inducibility of VT to clinical outcome (appropriate ICD therapy) in patients. Twelve patients had at least one appropriate ICD therapy for VT at follow-up. For both MCLE and IR-FGRE, the outcomes of the simulations of VT were significantly associated with the events of appropriate ICD therapy. This indicates that, as compared to conventional measurements such as LV EF, the simulations of VT corresponding to both MCLE and IR-FGRE were more sensitive in predicting appropriate ICD therapy in post-MI 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 | 0.000 | 0.004 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".