Increased Sensitivity of the European Medicines Agency Algorithm for Classification of Childhood Granulomatosis with Polyangiitis
Bibliographic record
Abstract
OBJECTIVE: Granulomatosis with polyangiitis (Wegener's; GPA) and other antineutrophil cytoplasmic antibody (ANCA)-associated vasculitides (AAV) are rare in childhood and are sometimes difficult to discriminate. We compared use of adult-derived classification schemes for GPA against validated pediatric criteria in the ARChiVe (A Registry for Childhood Vasculitis e-entry) cohort, a Childhood Arthritis and Rheumatology Research Alliance initiative. METHODS: Time-of-diagnosis data for children with physician (MD) diagnosis of AAV and unclassified vasculitis (UCV) from 33 US/Canadian centers were analyzed. The European Medicines Agency (EMA) classification algorithm and European League Against Rheumatism/Paediatric Rheumatology International Trials Organisation/Paediatric Rheumatology European Society (EULAR/PRINTO/PRES) and American College of Rheumatology (ACR) criteria for GPA were applied to all patients. Sensitivity and specificity were calculated (MD-diagnosis as reference). RESULTS: MD-diagnoses for 155 children were 100 GPA, 25 microscopic polyangiitis (MPA), 6 ANCA-positive pauciimmune glomerulonephritis, 3 Churg-Strauss syndrome, and 21 UCV. Of these, 114 had GPA as defined by EMA, 98 by EULAR/PRINTO/PRES, and 87 by ACR. Fourteen patients were identified as GPA by EULAR/PRINTO/PRES but not by ACR; 3 were identified as GPA by ACR but not EULAR/PRINTO/PRES. Using the EMA algorithm, 135 (87%) children were classifiable. The sensitivity of the EMA algorithm, the EULAR/PRINTO/PRES, and ACR criteria for classifying GPA was 90%, 77%, and 69%, respectively, with specificities of 56%, 62%, and 67%. The relatively poor sensitivity of the 2 criteria related to their inability to discriminate patients with MPA. CONCLUSION: EULAR/PRINTO/PRES was more sensitive than ACR criteria in classifying pediatric GPA. Neither classification system has criteria for MPA; therefore usefulness in discriminating patients in ARChiVe was limited. Even when using the most sensitive EMA algorithm, many children remained unclassified.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".