Adjudicating the Diagnosis of Immune Thrombocytopenia in a Clinical Research Study
Bibliographic record
Abstract
Immune thrombocytopenia (ITP) is a common autoimmune bleeding disorder. Establishing the diagnosis of ITP is challenging due to the lack of a reliable biomarker and nonspecific clinical criteria.[1] [2] A diagnosis of exclusion, ITP can only be established once other thrombocytopenic syndromes have been excluded. As a result, patients presenting with thrombocytopenia are often misdiagnosed as having or not having ITP, which can lead to delays in proper care and inappropriate use of immune-suppressant therapies.[3] To improve diagnostic accuracy, we implemented an adjudication process for the diagnosis of thrombocytopenic disorders in a clinical registry based on previous clinical studies[4] and consensus statements.[5] The objective of this study was to describe the methods and process of adjudicating the diagnosis of thrombocytopenia in a research context.
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How this classification was reachedexpand
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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".