Seasonal Variations and Their Influence on Antineutrophil Cytoplasmic Antibody–Associated Vasculitis Relapse
Bibliographic record
Abstract
To the Editor: We read with interest the recent publication by Kobayashi and colleagues on the seasonal effects on relapse of antineutrophil cytoplasmic antibody–associated vasculitis (AAV).1 The authors concluded that AAV relapse was influenced by seasonal variations and was frequently observed in the summer.1 We support and appreciate the authors’ work and agree with their conclusions but have some concerns about some of the details in the article. First, the etiology and pathogenesis of AAV are multifactorial, influenced by genetic and environmental factors, as well as by responses from the innate and adaptive immune systems. Many studies have confirmed that the onset of AAV is closely related to seasonal changes, but the specific results are not consistent. Studies have … Address correspondence to Dr. Z. Liu, Department of Rheumatology and Immunology, The Second Affiliated Hospital of Soochow University, Suzhou 215004, China. Email: zcliurheu{at}suda.edu.cn.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".