The Association Between Age at Diagnosis and Disease Characteristics and Damage in Patients With <scp>ANCA‐Associated</scp> Vasculitis
Bibliographic record
Abstract
OBJECTIVE: This study examined the relationship between age at diagnosis and disease characteristics and damage in patients with antineutrophil cytoplasmic antibody (ANCA)-associated vasculitis (AAV). METHODS: Analysis of a prospective longitudinal cohort of patients with granulomatosis with polyangiitis (GPA), microscopic polyangiitis (MPA), and eosinophilic GPA (EGPA) in the Vasculitis Clinical Research Consortium (2013-2021). Disease cohorts were divided by age at diagnosis (years): children (<18), young adults (18-40), middle-aged adults (41-65), and older adults (>65). Data included demographics, ANCA type, clinical characteristics, Vasculitis Damage Index (VDI) scores, ANCA Vasculitis Index of Damage (AVID) scores, and novel disease-specific and non-disease-specific damage scores built from VDI and AVID items. RESULTS: Analysis included data from 1020 patients with GPA/MPA and 357 with EGPA. Female predominance in GPA/MPA decreased with age at diagnosis. AAV in childhood was more often GPA and proteinase 3-ANCA positive. Children with GPA/MPA experienced more subglottic stenosis and alveolar hemorrhage; children and young adults with EGPA experienced more alveolar hemorrhage, need for intubation, and gastrointestinal involvement. Older adults (GPA/MPA) had more neurologic manifestations. After adjusting for disease duration, medications, tobacco, and ANCA, all damage scores increased with age at diagnosis for GPA/MPA (P < 0.001) except the disease-specific damage score, which did not differ (P = 0.44). For EGPA, VDI scores increased with age at diagnosis (P < 0.009), whereas all other scores were not significantly different. CONCLUSION: Age at diagnosis is associated with clinical characteristics in AAV. Although VDI and AVID scores increase with age at diagnosis, this is driven by non-disease-specific damage items.
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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 | 0.000 | 0.002 |
| 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".