Drug-induced QT prolongation and proarrhythmia: an inevitable link?
Bibliographic record
Abstract
One of the most feared potential adverse effects of many drugs is life threatening or fatal arrhythmia--particularly torsade de pointes (TdP) ventricular tachycardia in conjunction with QT prolongation. To fully understand the implications of QT prolongation, it is essential to have an understanding of the ion currents that comprise repolarization and their relation to electrophysiological abnormalities associated with TdP. Also, the QT interval is subject to patient-specific and sometimes idiosyncratic variability. The following questions are addressed: How close is the relationship between QT prolongation and proarrhythmia? How accurately do QT-interval measurements reflect cardiac repolarization? How representative is a single QT measurement with respect to the QT response to a drug? The presumed relationship between the QT interval and myocardial repolarization will be deconstructed, demonstrating that most of the important aspects of repolarization, and subsequent arrhythmogenesis, cannot be understood only through the simple numerical measurement of the QT interval. Repolarization reserve is also discussed. Suggestions for refining the understanding of drug-induced QT prolongation, TdP, and shortcomings of some current definitions are outlined. We speculate on possible future developments in understanding this relationship.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".