Bibliographic record
Abstract
The QT interval in electrocardiogram (ECG) reflects the total duration of ventricular myocardial depolarization and repolarization. It has been well recognized that many condition may cause QT interval prolongation. Unfortunately, numbers of cardiac and non-cardiac drug prolong the QT interval and cause a distinctive polymorphic ventricular tachycardia termed torsade de pointes (TdP). TdP can degenerate into ventricular fibrillation, which leads to sudden cardiac death. Recently various regulatory and clinical bodies of Europe, USA, Canada and Australia have made their focus on the drugs that induce prolongation of QT interval. Committee for Proprietary Medicinal Products (CPMP) of the European Agency issued a document entitled 'Points to Consider: The assessment of the potential for QT interval prolongation by non-cardiovascular medicinal products' [1, 2]. In addition, USFDA adopted the guideline 'Clinical evaluation of QT/QTc interval prolongation and proarrhythmic potential for non-anti arrhythmic drugs' [3]. These documents and guidelines are primarily concern with development of novel agents and the new use or new dose of already approved drugs. The scope of this guideline is to study the effect of drugs on QT prolongation and give idea of evaluation of drug's effects on QT prolongation. Today more than 50 available drugs (both old and new) have been identify, which prolong the QT interval [1]. Several drugs have been withdrawn from many countries on this basis but many of these drugs are still available in Indian market and potentially creating life-threatening arrhythmias. This article will focus on recommendation of study on the normal limits of QT interval in Indian population and preparation of the database, which can be helpful in withdrawal of drugs from the market that produces QT prolongation.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".