Personalisation of Action Potentials Based on Activation Recovery Intervals in Post-Infarcted Pigs: A Simulation Study
Why this work is in the frame
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Bibliographic record
Abstract
Cardiac modeling is a powerful and robust tool in electrophysiology (EP), supporting non-invasive arrhythmia diagnosis and therapy planning.Some studies showed that in silico modeling can be used to predict scar-related arrhythmia risk and ablation targets.However, model personalisation still relies on "average" EP parameters derived from literature, largely due to a paucity of their identification from EP data.We posit that activation-recovery interval (ARI), a surrogate for action potential duration (APD), can be extracted from intracardiac electrograms (iEGMs) and used to parameterize models for more accurate AP wave simulations per individual case.In this work we personalised APDs using ARI values extracted from endocardial electro-anatomical maps recorded in sinus rhythm in post-infarcted swine (n=8).We sought to investigate the differences in model parameters needed to calibrate simulated APDs in healthy tissue and border zone, BZ (i.e., arrhythmia substrate) when using an "average" ARI computed from all cases versus those calibrated from ARIs extracted per case.Results showed that average ARIs in healthy tissue and BZ for all cases were 206.12 50.18 ms and 213.21 52.1 ms, respectively.This work underlines the importance of model personalisation by case, suggesting that is fundamentally needed to accurately reproduce in silico the experimental observations.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| 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 | 0.000 | 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 it