Do we need to adjudicate major clinical events?
Bibliographic record
Abstract
PURPOSE: The use of centralized systems to adjudicate clinical events is common in large clinical trials, in spite of relatively little published literature concerning the rationale and justification. The purpose of this manuscript is to review the reasons for central adjudication and to discuss whether trials could be simplified by limiting or streamlining the adjudication process. METHODS: We reviewed the literature concerning central adjudication and documented the experience of adjudication in several clinical trials. Since definitions for nonfatal events are generally heterogeneous and subjective, one reason for a central process of adjudication is to assist in assuring systematic application of the definition used in the trial. In open-label trials, assuring that the adjudication is done blinded to treatment assignment may provide protection against differential misclassification. Regulatory authorities, including the FDA, derive confidence in the validity of results when central adjudication is performed. The clinical community has become accustomed to a certain amount of adjudication and may criticize trials that lack adjudication. LIMITATIONS: It is difficult to document the value of adjudication in trials that have reported adjudicated and nonadjudicated event rates and related treatment effects. Making rationale decisions about when and how to adjudicate is hampered by the lack of published study of when and how central adjudication is helpful to improve the quality and validity of trials and at what cost. CONCLUSIONS: Adjudication has not been shown to improve the ability to determine treatment effects. Thus, adjudication may be overly complex and overused in many large simple trials. The appropriate role of central adjudication - which trials, which outcomes, what methods - deserves scrutiny and further study.
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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 | 0.500 | 0.752 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.004 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.003 | 0.015 |
| Scholarly communication | 0.011 | 0.016 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.008 | 0.017 |
| Insufficient payload (model declined to judge) | 0.007 | 0.006 |
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".