Breakthrough Wave Detection in a 3D Computer Model of Atrial Endo-Epicardial Dissociation
Bibliographic record
Abstract
Experimental and clinical mapping of atrial fibrillation has revealed the occurrence of breakthrough activation patterns.These focal waves have been associated with endo-epicardial (endo-epi) dissociation and three-dimensional anatomical structures.To assess breakthrough detection techniques in computer models of atrial fibrillation, we created a 3D cubic-mesh atrial model with locally controllable endo-epi dissociation.In this model, epi and endo layers were electrically coupled only at randomly-distributed discrete connection sites.Eighteen endo-epi connection patterns were generated.Dedicated finite-difference numerical methods were developed to handle these discontinuities in conduction.These configurations were designed to generate breakthroughs at predictable locations.We developed a breakthrough detection algorithm based on full-resolution activation maps of both the epi-and endocardial surfaces.Wave tracking was used to calculate the lifespan of breakthroughs.Nonpropagating passive responses and breakthroughs with too short lifespan were eliminated.The approach was manually and automatically validated in 48 episodes of fibrillation in models with varying number of endo-epi connections.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".