Evaluation of the QT Interval in Patients with Drug-induced QT Prolongation and Torsades de Pointes
Bibliographic record
Abstract
Background Data on the optimal location of the ECG leads for the diagnosis of drug-induced long QT syndrome (diLQTS) with Torsades de Pointes (TdP) are lacking. Methods We systematically reviewed the literature for ECGs of patients with diLQTS and subsequent TdP. We assessed T-wave morphology in each lead and measured the longest QT interval in the limb and chest leads in a standardized fashion. Results Of 84 patients, 61.9% were female and mean age was 58.8 years. QTc was significantly longer in chest versus limb leads (mean (standard deviation) 671 (102) vs 655 (97) ms, p=0.02). Using only limb leads for QT interpretation, 18 (21.4%) ECGs were non-interpretable: 10 (11.9%) due to too flat T-waves, 7 (8.3%) due to frequent, early PVCs and 1 (1.2%) due to too low ECG recording quality. In the chest leads, ECGs were non-interpretable in 9 (10.7%) patients: 6 (7.1%) due to frequent, early PVCs, 1 (1.2%) due to insufficient ECG quality, 2 (2.4%) due to missing chest leads but none due to too flat T-waves. The most common T-wave morphologies in the limb leads were flat (51.0%), broad (14.3%) and late peaking (12.6%) T-waves. Corresponding chest lead morphologies were inverted (35.5%), flat (19.6%) and biphasic (15.2%) T-waves. Conclusions Our results indicate that QT evaluation by limb leads only underestimates the incidence of diLQTS experiencing TdP and favors the screening using both limb and chest lead ECG.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| 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".