Predictive factors of severe infections in patients with systemic necrotizing vasculitides: data from 733 patients enrolled in five randomized controlled trials of the French Vasculitis Study Group
Bibliographic record
Abstract
OBJECTIVES: Infections remain a major cause of morbidity and mortality in systemic necrotizing vasculitides (SNV). We aimed to identify factors predicting severe infections (SI) in SNV. METHODS: Data from five randomized controlled trials (RCTs) enrolling 733 patients were pooled. The primary end point was the occurrence of SI, defined by the need of a hospitalization and/or intravenous anti-infectious treatment and/or leading to death. RESULTS: After a median follow-up of 5.2 (interquartile range 3-9.7) years, 148 (20.2%) patients experienced 189 SI, and 98 (66.2%) presented their first SI within the first 2 years. Median interval from inclusion to SI was 14.9 (4.3-51.7) months. Age ≥65 years (hazard ratio (HR) 1.49 [1.07-2.07]; P=0.019), pulmonary involvement (HR 1.82 [1.26-2.62]; P=0.001) and Five Factor Score ≥1 (HR 1.21 [1.03-1.43]; P=0.019) were independent predictive factors of SI. Regarding induction therapy, the occurrence of SI was associated with the combination of GCs and CYC (HR 1.51 [1.03-2.22]; P = 0.036), while patients receiving only GCs were less likely to present SI (HR 0.69 [0.44-1.07]; P = 0.096). Finally, occurrence of SI had a significant negative impact on survival (P<0.001). CONCLUSION: SI in SNV are frequent and impact mortality. Age, pulmonary involvement and Five Factor Score are baseline independent predictors of SI. No therapeutic regimen was significantly associated with SI but patients receiving glucocorticoids and CYC as induction tended to have more SI.
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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 | 0.016 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.010 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".