Use of nonbiologic disease‐modifying antirheumatic drugs and risk of infection in patients with rheumatoid arthritis
Bibliographic record
Abstract
OBJECTIVE: Rheumatoid arthritis (RA) is associated with increased frequency of and mortality from infections, which may be related to host factors, RA itself, inflammation, or medication side effects. This study was undertaken to determine the effect of nonbiologic disease-modifying antirheumatic drugs (DMARDs) on infection risk in RA. METHODS: We performed a retrospective, longitudinal study of a population-based RA cohort in British Columbia, Canada, followed from January 1996 to March 2003 using administrative data. We evaluated mild infections (requiring a physician visit or antibiotics) and serious infections (requiring or complicating hospitalization). Adjusted risk of mild and serious infections associated with DMARD exposure was estimated using generalized estimating equation extension of multivariate Poisson regression models, after adjusting for baseline covariates (age, sex, RA duration, socioeconomic status) and time-dependent covariates (corticosteroids, comorbidity, prior infections). RESULTS: A total of 27,710 individuals with RA provided 162,710 person-years of followup. Of these, 25,608 (92%) had at least 1 mild infection and 4,941 (18%) had at least 1 serious infection. Use of DMARDs without corticosteroids was associated with a small decrease in mild infection risk of statistical significance but unclear clinical significance (adjusted rate ratio [RR] 0.90, 95% confidence interval [95% CI] 0.88-0.93 relative to no corticosteroid or DMARD use). Use of DMARDs without corticosteroids was not associated with increased serious infection risk (adjusted RR 0.92, 95% CI 0.85-1.0). Use of corticosteroids increased the risk of mild and serious infections. CONCLUSION: Our results indicate that use of nonbiologic DMARDs, including methotrexate, does not increase the risk of infection in RA, whereas use of corticosteroids does. This has important implications for counseling individuals with RA concerning risks and benefits of DMARDs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".