Rate of infections in severe necrotising vasculitis patients treated with cyclophosphamide induction therapy: a meta-analysis.
Bibliographic record
Abstract
OBJECTIVES: Infections are common complications of necrotising vasculitis. We aimed to determine the rate of infections in patients with severe necrotising vasculitis treated with cyclophosphamide (CYC) combined with high dose glucocorticoids (GC). METHODS: Searches of MEDLINE, Embase and Cochrane Library databases (1990 to May 2016) were performed. Inclusion criteria were randomised controlled trials of intravenous (IV) or oral (PO) CYC induction therapy for granulomatosis and polyangiitis (GPA), microscopic poyangiitis (MPA), eosinophilic granulomatosis with polyangiitis (EGPA), and systemic polyarteritis nodosa (PAN). Pooled rates of infectious complications were determined by random effects meta-analyses. Meta-regression was performed to identify variables associated with severe infection. RESULTS: Search results yielded 2636 references; 14 studies with a total of 888 subjects met inclusion criteria. The mean age of participants ranged from 39 to 75 years. Mean cumulative doses of CYC were 2.7 to 50.4 g and of GC were 6 to 13 g. The pooled rate per year per gram of CYC of severe infection was 2.2% (95% CI: 0.9, 5.3%, I2 = 58.7%), any infection was 5.6% (95% CI: 1.8, 16.7%, I2 = 79.1%) and infection-related deaths was 1.7% (95% CI: 0.8, 3.9%, I2 = 0%). By meta-regression, age, creatinine and cumulative GC dose were not significantly associated with the rate of severe infections. CONCLUSIONS: The rate of severe infections and infection related mortality in patients with severe necrotising vasculitis treated with CYC + GC induction therapy is high.
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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 | 0.017 | 0.028 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.017 | 0.068 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".