Understanding Expert Disagreement in Medical Data Analysis through Structured Adjudication
Bibliographic record
Abstract
Expert disagreement is pervasive in clinical decision making and collective adjudication is a useful approach for resolving divergent assessments. Prior work shows that expert disagreement can arise due to diverse factors including expert background, the quality and presentation of data, and guideline clarity. In this work, we study how these factors predict initial discrepancies in the context of medical time series analysis, examining why certain disagreements persist after adjudication, and how adjudication impacts clinical decisions. Results from a case study with 36 experts and 4,543 adjudicated cases in a sleep stage classification task show that these factors contribute to both initial disagreement and resolvability, each in their own unique way. We provide evidence suggesting that structured adjudication can lead to significant revisions in treatment-relevant clinical parameters. Our work demonstrates how structured adjudication can support consensus and facilitate a deep understanding of expert disagreement in medical data analysis.
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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 | 0.536 | 0.754 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.012 | 0.006 |
| Science and technology studies | 0.008 | 0.017 |
| Scholarly communication | 0.013 | 0.014 |
| Open science | 0.006 | 0.017 |
| Research integrity | 0.006 | 0.012 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".