Clinical events classification (CEC) in clinical trials: Report on the current landscape and future directions — proceedings from the CEC Summit 2018
Bibliographic record
Abstract
IMPORTANCE: Clinical events adjudication is pivotal for generating consistent and comparable evidence in clinical trials. The methodology of event adjudication is evolving, but research is needed to develop best practices and spur innovation. OBSERVATIONS: A meeting of stakeholders from regulatory agencies, academic and contract research organizations, pharmaceutical and device companies, and clinical trialists convened in Chicago, IL, for Clinical Events Classification (CEC) Summit 2018 to discuss key topics and future directions. Formal studies are lacking on strategies to optimize CEC conduct, improve efficiency, minimize cost, and generally increase the speed and accuracy of the event adjudication process. Major challenges to CEC discussed included ensuring rigorous quality of the process, identifying safety events, standardizing event definitions, using uniform strategies for missing information, facilitating interactions between CEC members and other trial leadership, and determining the CEC's role in pragmatic trials or trials using real-world data. Consensus recommendations from the meeting include the following: (1) ensure an adequate adjudication infrastructure; (2) use negatively adjudicated events to identify important safety events reported only outside the scope of the primary endpoint; (3) conduct further research in the use of artificial intelligence and digital/mobile technologies to streamline adjudication processes; and (4) emphasize the importance of standardizing event definitions and quality metrics of CEC programs. CONCLUSIONS AND RELEVANCE: As novel strategies for clinical trials emerge to generate evidence for regulatory approval and to guide clinical practice, a greater understanding of the role of the CEC process will be critical to optimize trial conduct and increase confidence in the data generated.
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 | 0.485 | 0.496 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.011 | 0.009 |
| Science and technology studies | 0.008 | 0.008 |
| Scholarly communication | 0.031 | 0.017 |
| Open science | 0.009 | 0.021 |
| Research integrity | 0.017 | 0.026 |
| Insufficient payload (model declined to judge) | 0.015 | 0.012 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".