The effect of low potassium in Brugrada Syndrome: A simulation study
Bibliographic record
Abstract
Brugada Syndrome (BrS) is associated with an increased risk of ventricular arrhythmias. It is caused by ion channel abnormalities and is characterized by coved ST elevation, J wave appearance and negative T waves in the right precordial electrocardiographic lead. The changes in the electrocardiogram (ECG) in the setting of BrS can be due to reduced inward currents, increased outward currents (I to ) and fibrosis. Some clinical reports relate hypokalemia to arrhythmic events (in the presence of other pathologies). Hypokalemia contributes to ST-segment elevation and changes in the T-wave morphology. It seems plausible that in patients with BrS, low extracellular potassium concentration ([K+] o ) might increase repolarization gradients, especially in right ventricle (RV), setting the stage for ventricular arrhythmia. The main goal of this study is evaluate the effect of low [K+] o in BrS using a mathematical modeling approach. Our results show that transmural dispersion of repolarization (TDR) was augmented in hypokalemic conditions. Furthermore, T peak -T end interval was calculated from the pseudo-ECG and was increased by 14 and 51 % in BrS and in BrS combined with hypokalemia, respectively, with respect to control. Additionally, a prominent J wave was observed in BrS and this increased if hypokalemia was also introduced.
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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 | 0.002 |
| 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 | 0.000 | 0.000 |
| 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".