The association between systemic glucocorticoid therapy and the risk of infection in patients with rheumatoid arthritis: systematic review and meta-analyses
Bibliographic record
Abstract
INTRODUCTION: Infection is a major cause of morbidity and mortality in patients with rheumatoid arthritis (RA). The objective of this study was to perform a systematic review and meta-analysis of the effect of glucocorticoid (GC) therapy on the risk of infection in patients with RA. METHODS: A systematic review was conducted by using MEDLINE, EMBASE, CINAHL, and the Cochrane Central Register of Controlled Trials database to January 2010 to identify studies among populations of patients with RA that reported a comparison of infection incidence between patients treated with GC therapy and patients not exposed to GC therapy. RESULTS: In total, 21 randomised controlled trials (RCTs) and 42 observational studies were included. In the RCTs, GC therapy was not associated with a risk of infection (relative risk (RR), 0.97 (95% CI, 0.69, 1.36)). Small numbers of events in the RCTs meant that a clinically important increased or decreased risk could not be ruled out. The observational studies generated a RR of 1.67 (1.49, 1.87), although significant heterogeneity was present. The increased risk (and heterogeneity) persisted when analyses were stratified by varying definitions of exposure, outcome, and adjustment for confounders. A positive dose-response effect was seen. CONCLUSIONS: Whereas observational studies suggested an increased risk of infection with GC therapy, RCTs suggested no increased risk. Inconsistent reporting of safety outcomes in the RCTs, as well as marked heterogeneity, probable residual confounding, and publication bias in the observational studies, limits the opportunity for a definitive conclusion. Clinicians should remain vigilant for infection in patients with RA treated with GC therapy.
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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 | 0.043 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.034 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".