Severe Infection in Antineutrophil Cytoplasmic Antibody-associated Vasculitis
Bibliographic record
Abstract
OBJECTIVE: To compare the rate of severe infections after the onset of antineutrophil cytoplasmic antibody (ANCA)-associated vasculitis (AAV) with the rate in the background population, and to identify predictors of severe infections among patients with AAV. METHODS: The study cohort was 186 patients with AAV diagnosed from 1998 to 2010, consisting of all known cases in a defined population in southern Sweden. For each patient, 4 age-and sex-matched reference subjects were randomly chosen from the background population. Using the Skåne Healthcare Register, all International Classification of Diseases codes of infections assigned from 1998 to 2011 were identified. Severe infections were defined as infectious episodes requiring hospitalization. Rate ratios were calculated by dividing the rate in AAV by the rate among the reference subjects. RESULTS: 5.35 (1.54-23.8), nonspecific septicemia 4.55 (1.60-13.8), and skin 5.35 (1.69-19.8). Of the severe infections, 38.4% occurred within 6 months of diagnosis, 30.2% from 7-24 months, and 31.4% after 24 months. High serum creatinine and older age at diagnosis were associated with severe infection (p < 0.001). Of those with severe infection, 46.5% died during followup compared to 26% of patients without severe infection (p = 0.004). CONCLUSION: Patients with AAV have markedly higher rates of severe infection compared with the background population, especially patients with older age and impaired renal function. The risk of severe infection is particularly high in the first 6 months following the diagnosis of vasculitis.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".