Seasonal Influence on Development of Antineutrophil Cytoplasmic Antibody–Associated Vasculitis: A Retrospective Cohort Study Conducted at Multiple Institutions in Japan (J-CANVAS)
Bibliographic record
Abstract
Objective To clarify seasonal and other environmental effects on the onset of antineutrophil cytoplasmic antibody (ANCA)–associated vasculitis (AAV). Methods We enrolled patients with new-onset eosinophilic granulomatosis with polyangiitis (EGPA), microscopic polyangiitis (MPA), and granulomatosis with polyangiitis (GPA) registered in the database of a Japanese multicenter cohort study. We investigated the relationship between environmental factors and clinical characteristics. Seasons were divided into 4 (spring, summer, autumn, and winter), and the seasonal differences in AAV onset were analyzed using Pearson chi-square test, with an expected probability of 25% for each season. Results A total of 454 patients were enrolled, with a mean age of 70.9 years and a female proportion of 55.5%. Overall, 74, 291, and 89 patients were classified as having EGPA, MPA, and GPA, respectively. Positivity for myeloperoxidase (MPO)-ANCA and proteinase 3 (PR3)-ANCA was observed in 355 and 46 patients, respectively. Overall, the seasonality of AAV onset significantly deviated from the expected 25% for each season (P= 0.001), and its onset was less frequently observed in autumn. In ANCA serotypes, seasonality was significant in patients with MPO-ANCA (P< 0.001), but not in those with PR3-ANCA (P= 0.97). Additionally, rural residency of patients with AAV was associated with PR3-ANCA positivity and biopsy-proven pulmonary vasculitis. Conclusion The onset of AAV was influenced by seasonal variations and was less frequently observed in autumn. In contrast, the occurrence of PR3-ANCA was triggered, not by season, but by rural residency.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".