Regional risk of tuberculosis and viral hepatitis with tumor necrosis factor-alpha inhibitor treatment: A systematic review
Bibliographic record
Abstract
Background: TNFα inhibitors are regularly used to treat autoimmune diseases. Tuberculosis (TB) and viral hepatitis B are considered potential infectious complications, and screening and surveillance are therefore recommended. Current guidelines do not take into account regional differences in endemicity of these infections. Methods: A systematic literature review of TB and viral hepatitis in patients receiving TNFα-inhibitors was performed, searching in PubMed, Embase, MEDLINE and Web of Science databases. Studies were selected against predefined eligibility criteria and assessed using the Newcastle-Ottawa scale. The number of TB and viral hepatitis cases/1,000 TNFα-inhibitor patients were evaluated, and regional variation compared. Results: 105 observational studies involving over 140,000 patients were included. Overall, 1% of patients developed TB or viral hepatitis B. TB cases/1,000 TNFα-inhibitor patients were 4-fold higher in Asia, Africa, and South America than in Europe, North America, and Australasia where only 0%–0.4% of patients developed TB. Hepatitis B cases/1,000 patients were over 15-fold higher in countries with high prevalence (China, Taiwan, South Korea, Thailand) compared with low prevalence (p < 0.00001) where only 0.4% of patients developed hepatitis B. Only three of 143 patients developed viral hepatitis C, and there was insufficient data to allow regional sub-analysis. Conclusion: TB and viral hepatitis B infections in patients treated with TNFα inhibitors are largely confined to countries with high prevalence of these infections. As only 1/2,500 patients in low prevalence countries treated with TNFα inhibitors develop TB or viral hepatitis B, we suggest an individualized, risk-based approach, rather than universal screening for all patients.
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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".