Incidence of Malignancy Prior to Antineutrophil Cytoplasmic Antibody–associated Vasculitis Compared to the General Population
Bibliographic record
Abstract
OBJECTIVE: Previous studies have reported an increased malignancy risk preceding antineutrophil cytoplasmic antibody-associated vasculitis (AAV), suggesting common pathogenic pathways in these 2 entities. However, the study results were conflicting and often limited to patients with granulomatosis with polyangiitis (GPA). Here, we study the malignancy risk prior to AAV diagnosis [either GPA or microscopic polyangiitis (MPA)] to elaborate on the putative association between malignancy and AAV. METHODS: A total of 203 patients were selected for the current study. Malignancies prior to AAV diagnosis were identified using a nationwide pathology database, and their occurrence was verified by reviewing the medical files of 145 patients (71.4%). The malignancy incidence was compared to the general population by calculation of standardized incidence ratios (SIR), matching for sex, age, and time period. SIR were calculated for 2 intervals: < 2 years and ≥ 2 years prior to AAV diagnosis. Separate analyses were performed for GPA and MPA. RESULTS: The overall risk for malignancy prior to AAV diagnosis was similar to that of the general population (SIR 0.96, 95% CI 0.55-1.57), as was true when risks were analyzed by malignancy type, including skin, bladder, kidney, lung, stomach, rectum, and uterus (SIR ranged from 1.64 to 4.14). We found no significant difference in malignancy risk between patients with GPA and MPA. CONCLUSION: Our findings do not support the hypothesis that preceding malignancies and AAV have a causal relationship or shared pathogenic pathways.
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 | 0.000 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".