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 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 | 0.001 | 0.003 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".