Suitability of Genetic Algorithm Generated Models to Simulate Atrial Fibrillation and K<sup>+</sup>Channel Blockades
Bibliographic record
Abstract
Channel modifications resulting from atrial fibrillation (AF) have received a great deal of attention over the last decade. Mathematical models can be used to help understand the significance of these changes. These models can be used to predict the responses of specific channel-blocker drugs on normal action potentials (NAPs) and action potentials (APs) present during chronic AF (AFAP). Unfortunately, to date, models are "average representations" of APs, but AP morphology varies significantly through the atria. To account for this natural heterogeneity, which plays a very important role in determining the nature of AF, we previously presented a genetic algorithm (GA) to automatically fit the conductance parameters of atrial model APs based upon experimentally measured APs. In this study, three automatically produced models from different canines were used to investigate the suitability of this technique in assessing the effects of AF and drug-related channel modifications.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".