The Risk of Tuberculosis in Patients with Rheumatoid Arthritis Treated with Tumor Necrosis Factor-α Antagonist: A Metaanalysis of Both Randomized Controlled Trials and Registry/Cohort Studies
Bibliographic record
Abstract
OBJECTIVE: Tumor necrosis factor-α (TNF-α) antagonists have significantly improved treatment results in rheumatoid arthritis (RA), but have also increased the risk of tuberculosis (TB). Etanercept (ETN), adalimumab (ADA), infliximab (IFX), golimumab, and certolizumab pegol are the 5 drugs currently available on the market. This article aimed to evaluate the risk of TB infection from these 5 drugs for patients with RA. METHODS: We searched PubMed, EMBASE, COCHRANE library, OVID, and EBSCO for randomized controlled trials (RCT) of TNF-α antagonist versus control and registry/longitudinal cohort studies of 1 TNF-α antagonist versus another. The Mantel-Haenszel test was adopted to analyze risk ratio (RR) in this metaanalysis. RESULTS: Fifty RCT and 13 non-RCT were included in this study. No significant difference in TB risk was found in the RCT because of the short observational periods. In the non-RCT, TNF-α antagonist was associated with a higher TB risk in patients with RA (RR 4.03, 95% CI 2.36-6.88), and the TB incidence rates of IFX and ADA were 2.78 and 3.88 times, respectively, higher than that of ETN. Further, preventive treatment for latent TB infection (LTBI) was shown to reduce the TB risk by 65% (RR 0.35, 95% CI 0.15-0.82). CONCLUSION: This study demonstrated a significant increase in TB risk in patients with RA treated with TNF-α antagonists; among them, ETN is least likely to cause active TB. The study also proposes the necessity of LTBI prophylaxis in patients with 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 | 0.032 | 0.041 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.022 | 0.059 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 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".