Do Adult Disease Severity Subclassifications Predict Use of Cyclophosphamide in Children with ANCA-associated Vasculitis? An Analysis of ARChiVe Study Treatment Decisions
Bibliographic record
Abstract
OBJECTIVE: To determine whether adult disease severity subclassification systems for antineutrophil cytoplasmic antibody-associated vasculitis (AAV) are concordant with the decision to treat pediatric patients with cyclophosphamide (CYC). METHODS: We applied the European Vasculitis Study (EUVAS) and Wegener's Granulomatosis Etanercept Trial (WGET) disease severity subclassification systems to pediatric patients with AAV in A Registry for Childhood Vasculitis (ARChiVe). Modifications were made to the EUVAS and WGET systems to enable their application to this cohort of children. Treatment was categorized into 2 groups, "cyclophosphamide" and "no cyclophosphamide." Pearson's chi-square and Kendall's rank correlation coefficient statistical analyses were used to determine the relationship between disease severity subgroup and treatment at the time of diagnosis. RESULTS: In total, 125 children with AAV were studied. Severity subgroup was associated with treatment group in both the EUVAS (chi-square 45.14, p < 0.001, Kendall's tau-b 0.601, p < 0.001) and WGET (chi-square 59.33, p < 0.001, Kendall's tau-b 0.689, p < 0.001) systems; however, 7 children classified by both systems as having less severe disease received CYC, and 6 children classified as having severe disease by both systems did not receive CYC. CONCLUSION: In this pediatric AAV cohort, the EUVAS and WGET adult severity subclassification systems had strong correlation with physician choice of treatment. However, a proportion of patients received treatment that was not concordant with their assigned severity subclass.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".