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 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 | 0.009 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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; 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".