355. FACTORS PREDICTING SEVERE INFECTIONS IN PATIENTS WITH SYSTEMIC NECROTIZING VASCULITIDES BASED ON DATA FROM 733 PATIENTS ENROLLED IN RANDOMIZED–CONTROLLED TRIALS
Bibliographic record
Abstract
Background: 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 5 RCTs enrolling 733 patients were pooled. The primary endpoint was the occurrence of SI, classified as grade ⩾3 as proposed by the CTCAE v.4.0. Results: Baseline characteristics are summarized in Table 1. At 2 years, pulmonary [OR 2.11 (1.18–3.77); P = 0.01] and nervous system [OR 1.82 (1.01– 3.30), P = 0.048] involvements were independent predictors of SI, while at 5-years, we found pulmonary involvement [OR 1.71 (1.10–2.65); P = 0.02] and age [OR 1.19 (1.02–1.38) per 10 years, P = 0.02] (Table 1). Renal failure was significantly associated with SI at 1 year (P = 0.02), but not at 2 and 5 years (P = 0.08). Compared with eGFR ⩾60 ml/min/1.73m2, HR for incident SI was 4.99 (1.80-13.9; P = 0.002) at 1 year with eGFR<15 ml/min/1.73m2, and decreased over time to 3.05 at 2-years and 2.56 at 5-years. No therapeutic regimen was significantly associated with SI, but treatment with cyclophosphamide then rituximab tended to have more SI. Based on baseline items associated with higher incidence of SI, we set up a combined score that could predict the risk of SI. Finally, occurrence of SI had a significant negative impact on mortality (P < 0.001). Conclusion: Severe infections in SNV are frequent and impact mortality. Age, pulmonary and neurological involvement are predictors of SI at 2 and 5 years, whereas severe renal failure is associated with SI during the first year. No therapeutic regimen was significantly associated with SI. Disclosures: None
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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 | 0.023 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.014 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".