Structural Defects Lead to Dynamic Entrapment in Cardiac Electrophysiology
Bibliographic record
Abstract
Biological networks are typically comprised of many parts whose interactions are governed by nonlinear dynamics. This potentially imbues them with the ability to support multiple attractors, and therefore to exhibit correspondingly distinct patterns of behavior. In particular, multiple attractors have been demonstrated for the electrical activity of the diseased heart in situations where cardioversion is able to convert a reentrant arrhythmia to a stable normal rhythm. Healthy hearts, however, are typically resilient to abnormal rhythms. This raises the question as to how a healthy cardiac cell network must be altered so that it can support multiple distinct behaviors. Here we demonstrate how anatomic defects can give rise to multi-stability in the heart as a function of the electrophysiological properties of the cardiac tissue and the timing of activation of ectopic foci. This leads to a form of hysteretic behavior, which we call dynamic entrapment, whereby the heart can become trapped in aberrant attractor as a result of a transient change in tissue properties. We show that this can lead to a highly inconsistent relationship between clinical symptoms and underlying pathophysiology, which raises the possibility that dynamic entrapment may underlie other forms of chronic idiopathic illness.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".