Fully automated initiation of simulated episodes of atrial arrhythmias
Bibliographic record
Abstract
AIMS: To develop computational tools for automatically initiating a large number of independent episodes of atrial arrhythmias in electro-anatomical computer models of the atria and therefore facilitating the design of in silico experiments. METHODS AND RESULTS: A biophysical model of the atria was constructed from segmented medical images of the human atria of a patient with atrial fibrillation (AF). A set of 40 initial conditions were generated based on a priori knowledge about wavefront propagation and the number and location of reentries (1-6 randomly distributed over the atrial epicardium). Simulations were run from each of these initial conditions in three substrates representing different forms of AF dynamics (stable rotors; multiple unstable meandering wavelets; and wavelets broken by repolarization heterogeneities). To demonstrate the applicability of the initiation method for testing clinical of therapeutic interventions, the channel I(Kr) was blocked after 2 s of simulation and its effect on the number of functional reentries was documented. The use of pre-computed initial conditions enabled to successfully generate episodes of simulated AF in each substrate. Blockade of I(Kr) channel prolonged action potential duration, resulting in a reduction of the number of functional reentries. In the substrate with unstable spiral waves, the effect was sufficiently large to terminate AF in about two-thirds of the cases. In the two other substrates, the effect was minor. CONCLUSION: These new simulation tools may help investigate in computer models therapeutic interventions in different substrates in order to identify substrate-specific optimal therapy.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".