Simulation of long QT syndrome 2 and its associated arrhythmia
Bibliographic record
Abstract
Computational cardiac models are an emerging technology that can offer unique insight into the field of medicine. Despite this, much progress remains to be made before they can be used in standard clinical practice. One significant challenge is in representing an individual’s particular disease presentation with standardized models. It is necessary to overcome this challenge in order for cardiac models to be practically beneficial to patients in a healthcare setting. In this study, we modify a computational cardiac model to observe the electrophysiological characteristics of a specific cardiac condition: long QT syndrome 2 (LQT2). We simulate the baseline cellular effects of LQT2 as well as the startle response that often triggers life-threatening arrhythmias in patients displaying this condition. Finally, a potential line of therapy for LQT2 is simulated, and significant changes in the cardiac cell action potentials are observed. The approach used demonstrates not only the feasibility of parametrizing cardiac models for disease states but also the benefit that cardiac models can offer to the current healthcare paradigm.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".