An Endpoint Adjudication Committee for the Assessment of Computed Tomography Scans in Fracture Healing
Bibliographic record
Abstract
Abstract The use of endpoint adjudication committees (EACs) has the potential to reduce subjectivity and potential bias in clinical research trials and contribute to a higher quality of research. In a recent randomized control trial (RCT), we used serial computed tomography (CT) imaging to visualize fracture healing of the scaphoid as a primary outcome. The scaphoid bone poses a challenge in the diagnosing of fractures and non-unions due to its complicated shape. An EAC was created to increase the quality of the data and the validity of our findings. While an adjudication process has long been proposed and described for X-rays, this study outlines a rational approach to CT scan adjudication for bone fracture healing. A total of 364 scans were acquired in the RCT and of these, 101 were adjudicated for a binary endpoint of union vs. non-union. The application of EACs such as described in this paper is a useful tool in orthopaedic research requiring the adjudication of fracture healing as a study outcome.
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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 | 0.746 | 0.785 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.009 | 0.011 |
| Insufficient payload (model declined to judge) | 0.007 | 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".