Infection outcomes in patients with rheumatoid arthritis treated with abatacept and other disease-modifying antirheumatic drugs: Results from a 10-year international post-marketing study
Bibliographic record
Abstract
OBJECTIVE: To evaluate risk of infections requiring hospitalization and opportunistic infections, including tuberculosis, in patients with rheumatoid arthritis (RA) treated with abatacept versus conventional synthetic (cs) disease-modifying antirheumatic drugs (DMARDs) and other biologic/targeted synthetic (b/ts) DMARDs. METHODS: Five international observational data sources were used: two biologic registries (Sweden, Germany), a disease registry (USA) and two healthcare claims databases (Canada, USA). Crude incidence rates (IRs) per 1000 patient-years, with 95 % CIs, were used to estimate rate ratios (RRs) comparing abatacept versus csDMARDs or other b/tsDMARDs. RRs were adjusted for demographic factors, comorbidities, and other potential confounders and then pooled across data sources using a random effects model (REM). RESULTS: The data sources included 6450 abatacept users, 136,636 csDMARD users and 54,378 other b/tsDMARD users, with a mean follow-up range of 2.2-6.2 years. Across data sources, the IRs for infections requiring hospitalization ranged from 16 to 56 for abatacept, 19-46 for csDMARDs, and 18-40 for other b/tsDMARDs. IRs for opportunistic infections were 0.4-7.8, 0.3-4.3, and 0.5-3.8; IRs for tuberculosis were 0.0-8.4, 0.0-6.0, and 0.0-6.3, respectively. The pooled adjusted RR (95 % CI), only reported for infections requiring hospitalization, was 1.2 (0.6-2.2) for abatacept versus csDMARDs and 0.9 (0.6-1.3) versus other b/tsDMARDs. CONCLUSIONS: Data from this international, observational study showed similar hospitalized infection risk for abatacept versus csDMARDs or other b/tsDMARDs. IRs for opportunistic infections, including tuberculosis, were low. These data are consistent with the known safety profile of abatacept.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".