Tuberculosis Risk in Patients Treated with Non-Anti-Tumor Necrosis Factor- (TNF- ) Targeted Biologics and Recently Licensed TNF- Inhibitors: Data from Clinical Trials and National Registries
Bibliographic record
Abstract
This review aimed to evaluate the risk of active tuberculosis (TB) occurrence in patients with rheumatic disorders receiving non-anti-tumor necrosis factor (TNF) targeted biologics anakinra (ANK), tocilizumab (TCZ), rituximab (RTX), abatacept (ABA), and recently approved anti-TNF golimumab (GOL), and certolizumab pegol (CTP). In recent findings, no cases of active TB were recorded in patients with rheumatoid arthritis (RA) and other rheumatic conditions treated with anti-CD20+ RTX and anti-CD28 ABA. No patient receiving anti-interleukin 1 (IL-1) ANK developed active TB, and an increased risk was excluded in a Canadian database. In contrast, 8 active TB cases were observed in 21 trials of patients with RA receiving anti-IL-6 TCZ, while no increased TB risk resulted from Japanese postmarketing surveillance. Among GOL-treated and CTP-treated patients, 8 and 10 active TB cases occurred, respectively, while no data are available from registries. However, all but 1 TB case recorded in patients treated with TCZ, GOL, and CTP occurred in TB-endemic countries. No TB risk resulted for ANK, RTX, and ABA, suggesting pretreatment screening procedures for latent TB infection detection are unnecessary. Because all TB cases occurred in countries at high risk for TB, where TB exposure could have occurred during treatment, no definitive conclusions can be drawn for TCZ, GOL, and CTP.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".