Antiproteinase-3 (PR3)-Antineutrophil Cytoplasmic Atibody (ANCA) as a Predictor for Relapse in Antineutrophil Cytoplasmic Antibody-Associated Vasculitis: A Meta-Analysis
Bibliographic record
Abstract
BACKGROUND:Several predictive markers are related to the relapse of antineutrophil cytoplasmic antibody (ANCA)-associated small-vessel vasculitis (AAV). However, the conclusions are not definite, especially for antiproteinase-3 ANCA (PR3-ANCA). This study evaluated the value of PR3-ANCA in predicting relapse of AAV. MATERIAL AND METHODS:Four databases (PubMed, Embase, Web of Science, and Cochrane Library) were searched. The cohort studies that assessed the factors associated with AAV relapse were included. Study quality was assessed using the Newcastle-Ottawa scale. The hazard ratios (HR) were analyzed using fixed-effects models. The sensitivity and publication bias analysis was performed to verify the reliability of the results. RESULTS:A total of 14 studies, including 2761 participants, were evaluated in this meta-analysis. Fixed-effects pooling showed a significant association between PR3-ANCA and the relapse of AAV (pooled HR 1.829, 95% CI 1.591-2.103) compared to MPO (myeloperoxidase)-ANCA. In addition, AAV patients with lung involvement were 1.6 times more likely to have relapse (pooled HR 1.635, 95% CI 1.324-2.064) compared to those without lung involvement. The other factors, including age, sex, kidney-limited disease, granulomatosis with polyangiitis, upper respiratory involvement, and serum creatinine, could not predict AAV relapse according to the results of fixed-effects pooling. CONCLUSIONS:PR3-ANCA and lung involvement could be used to predict occurrence of AAV relapse.
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 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 | 0.013 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.036 |
| Bibliometrics | 0.002 | 0.013 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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; both teacher heads agree on what is shown here.
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".