Adjudicating the Diagnosis of Immune Thrombocytopenia in a Clinical Research Study
Bibliographic record
Abstract
<b>Background:</b> Establishing the diagnosis of immune thrombocytopenia (ITP) is challenging in clinical practice and research settings even for experienced hematologists because it is a diagnosis of exclusion. <b>Methods:</b> We developed criteria to adjudicate the diagnosis of ITP using patients enrolled in the McMaster ITP Registry. At each patient visit, the cause of the thrombocytopenia was determined by the treating physician according to published criteria using all available information. We adjudicated the cause of the thrombocytopenia for any patient whose diagnosis was uncertain, if the diagnosis changed from one follow-up visit to another, or if the thrombocytopenia occurred in the context of pregnancy. Adjudication was done independently by one of the principal investigators, an external hematologist and a research associate using predefined criteria. <b>Results:</b> The etiology of the thrombocytopenia was adjudicated for 130 patients (n= 195 clinic visits). Reasons for adjudication were: a change in diagnosis from one visit to the next (n= 77; 59.2%), no clear cause of the thrombocytopenia was identified (n=46; 35.4%), and pregnancy-related thrombocytopenia (n=7; 5.4%). After adjudication, the most common changes in diagnosis were from primary ITP to secondary ITP (n=10), from “unknown” diagnosis to either primary ITP (n=15) or non-immune thrombocytopenia (n=10), or a change in the cause of non-immune thrombocytopenia (n=10). The diagnosis did not change for 38 patients (29.7%) after adjudication. <b>Conclusions:</b> Adjudication led to a more accurate diagnosis for 92 of 130 (70.8%) patients enrolled in the registry who presented with thrombocytopenia. This process can improve the clinical diagnosis of ITP.
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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".