Adalimumab, Etanercept, Infliximab, and the Risk of Tuberculosis: Data from Clinical Trials, National Registries, and Postmarketing Surveillance
Bibliographic record
Abstract
This review evaluates the risk of tuberculosis (TB), adherence with recommendations for TB prevention, and host-related risk in patients with rheumatoid arthritis (RA), psoriatic arthritis, and ankylosing spondylitis receiving infliximab (IFX), adalimumab (ADA), and etanercept (ETN) through an analysis of phase III randomized controlled trials (RCT), postmarketing surveillance, and national registries. Ten (0.21%) TB cases occurred among 4590 patients in 16 RCT of IFX, 9 (0.12%) among 7009 patients in 21 RCT of ADA, and 4 (0.05%) among 7741 patients in 26 RCT of ETN. Overall, 19/23 (83%) TB cases occurred in patients with RA. Data from national registries and postmarketing surveillance showed an increased risk of TB in patients receiving any of the 3 anti-tumor necrosis factor (TNF) drugs, with a 3-4 times higher risk associated with IFX and ADA than with ETN. Deviations from recommended TB prevention procedures were observed in up to 80% of patients, and most registries did not include data on host-related risk factors for TB. TB occurrence was reduced in recent RCT but not in real-life practice. TB risk was lower for ETN than for monoclonal antibody anti-TNF agents. More complete data collection, including host-related TB risk factors, is advisable to avoid biased results.
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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 | 0.070 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 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".