Rates of Serious Infections and Malignancies Among Patients with Rheumatoid Arthritis Receiving Either Tumor Necrosis Factor Inhibitor or Rituximab Therapy
Bibliographic record
Abstract
OBJECTIVE: Because of the role of tumor necrosis factor (TNF) in host defense, it was hypothesized that its inhibition might lead to an increased risk of malignancies and infections. The objective of our study was to assess the incidence of serious infections leading to hospitalization and malignancies among patients with rheumatoid arthritis (RA) receiving either TNF inhibitor or rituximab (RTX) therapy. METHODS: The study population was identified from the National Register for Biologic Treatment in Finland and the hospital records of Central Finland Central Hospital for conventional disease-modifying antirheumatic drug (cDMARD) users. Data on infections and malignancies were acquired from national healthcare registers. A Poisson model was used to calculate the adjusted incidence rate ratios (aIRR) and was composed of age, sex, time from diagnosis, year of the beginning of the followup, rheumatoid factor status, Disease Activity Score at 28 joints, Health Assessment Questionnaire, prior malignancy, prior serious infection, prior biologic use, and time-updated use of methotrexate, sulfasalazine, hydroxychloroquine, and oral corticosteroids as confounders. RESULTS: In total, during the followup of 10,994 patient-years, 92 malignancies and 341 serious infections were included in the analyses. The aIRR of infections compared to cDMARD users were 1.2 (95% CI 0.63-2.3), 0.84 (95% CI 0.53-1.3), 0.98 (95% CI 0.60-1.6), and 1.1 (95% CI 0.59-1.9) for the patients treated with infliximab (IFX), etanercept, adalimumab, and RTX, respectively. The crude rates of malignancies were highest among the users of cDMARD and RTX, and lowest among patients treated with IFX with no differences in aIRR. CONCLUSION: Our results provide some reassurance of the safety of biologic treatments in the treatment of RA.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.000 | 0.000 |
| 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".