Anti-rheumatic drug use and risk of serious infections in rheumatoid arthritis
Bibliographic record
Abstract
OBJECTIVES: To assess the risk of severe infections associated with the use of traditional disease-modifying anti-rheumatic drugs (DMARDs) and glucocorticoid agents in rheumatoid arthritis (RA). METHODS: Our study was a case-control design nested within a cohort of 23 733 RA patients studied between 1 January 1980 and 31 December 2003. Matching on age and gender, and adjusting for comorbidity and physician use, conditional logistic regression was used to estimate the effect of specific drugs on the rate ratio (RR) for infections requiring hospitalization. RESULTS: The risk for all infections requiring hospitalization appeared to be most elevated with current exposures to cyclophosphamide [RR: 3.26, 95% confidence interval (CI): 2.28-4.67] and systemic glucocorticoid agents (RR: 2.56, 95% CI: 2.29-2.85); azathioprine was associated with a moderate increased risk (RR: 1.52, 95% CI: 1.18-1.97). There was a suggestion of increased risk of pneumonia due to methotrexate (RR: 1.16, 95% CI: 1.02-1.33). The results were similar for the period before and after the introduction of anti-tumour necrosis factor (TNF) agents. The RR point estimate for anti-TNF agents suggested about a 2-fold increased risk for all infections, but the estimate was imprecise. CONCLUSIONS: In this large cohort of RA patients, the most heightened risk of serious infections was seen with the use of glucocorticoid agents and immunosuppressive DMARDs. Assessments of infection risk related to newer and emerging therapies should carefully consider concomitant medication exposures, including traditional DMARDs and glucocorticoid 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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".