Do we need to adjudicate major clinical events?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.197 | 0.928 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.042 | 0.018 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 0.011 |
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 it