Constructing Realistic Canine Bilayer Biatrial Mesh for the Modeling and Simulation of Atria Fibrillation
Bibliographic record
Abstract
An improved appreciation of the mechanisms underlying atrial fibrillation (AF) is essential for better arrhythmia management.A deeper understanding requires a full consideration of atrial geometry.Here, we develop the first anatomically accurate high-resolution canine atrial computational bilayer model for AF studies.Canine CT-scan imaging data were segmented and used to reconstruct the Bachmann's bundle (BB), the left atrium (LA) and right atrium (RA).The LA is dilated to obtain the second layer.The RA endocardial layer consists of the sinus node (SAN), the pectinate muscles (PMs) and the crista terminalis (CT).The Ramirez-Nattel-Courtemanche canine cell model was used to simulate electrical activity.Activation time (AT) and action potential duration (APD) were computed.The obtained bilayer mesh has high resolution with average edge length 276±58 µm.Action potential propagation from the SAN was realistic and its path along CS presumes an important role of the CS in the initiation and maintenance of rotors during AF.The propagation time from SAN to PVs was ~119 ms and APD90 was heterogeneous in the model.This new bilayer model with realistic geometry, combined with experimental data, will help to better understand AF and its underlying mechanisms, in order to develop better prognostic and therapeutic tools.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".