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 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".