Different effects of amiodarone and dofetilide on the dispersion of repolarization between well-coupled ventricular and Purkinje fibers<sup>1</sup>
Bibliographic record
Abstract
Increased transmural dispersion of repolarization is an established contributing factor to ventricular tachyarrhythmias. In this study, we evaluated the effect of chronic amiodarone treatment and acute administration of dofetilide in canine cardiac preparations containing electrotonically coupled Purkinje fibers (PFs) and ventricular muscle (VM) and compared the effects to those in uncoupled PF and VM preparations using the conventional microelectrode technique. Dispersion between PFs and VM was inferred from the difference in the respective action potential durations (APDs). In coupled preparations, amiodarone decreased the difference in APDs between PFs and VM, thus decreasing dispersion. In the same preparations, dofetilide increased the dispersion by causing a more pronounced prolongation in PFs. This prolongation was even more emphasized in uncoupled PF preparations, while the effect in VM was the same. In uncoupled preparations, amiodarone elicited no change on the difference in APDs. In conclusion, amiodarone decreased the dispersion between PFs and VM, while dofetilide increased it. The measured difference in APD between cardiac regions may be the affected by electrotonic coupling; thus, studying PFs and VM separately may lead to an over- or underestimation of dispersion.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".