The use of electrocardiograms in clinical trials: a public discussion of the proposed ICH E14 regulatory guidance
Bibliographic record
Abstract
This meeting, jointly sponsored by the FDA, Drug Information Association and Heart Rhythm Society, examined crucial issues on nonclinical and clinical evaluation of the potential of new drugs to prolong the QT interval of an electrocardiogram (ECG). It gathered approximately 350 attendees from pharmaceutical industry, academia, core ECG analysis laboratories, regulatory agencies (FDA, European Medicines Agency, Japanese Ministry of Health, Labour and Welfare, and Health Canada) and the International Conference on Harmonisation (ICH). Key issues discussed included the reliability of the S7B guideline strategy, design and usefulness of the 'thorough QT/QTc study' recommended by ICH E14 guideline, choice of 5 ms QTc prolongation as a threshold for regulatory concern, ECG reading, and statistical analysis. This report is restricted to the two main presentations dealing with the predictability of nonclinical tests for clinical outcomes--one defending the prognostic value of nonclinical tests and the other, from the FDA, which casts reservations on the predictive value of nonclinical studies. Commentary on the recent finalisation of ICH S7B and E14 guidelines are also provided.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.348 | 0.419 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.007 | 0.024 |
| Scholarly communication | 0.017 | 0.015 |
| Open science | 0.013 | 0.008 |
| Research integrity | 0.120 | 0.069 |
| Insufficient payload (model declined to judge) | 0.003 | 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".