Epstein-Barr Virus (EBV) Prevalence and the Risk of Reactivation in Patients with Inflammatory Arthritis Using Anti-TNF Agents and in those who are Biologic Naive
Bibliographic record
Abstract
OBJECTIVE: Anti-TNF agents (etanercept, infliximab and adalimumab) are widely used in inflammatory conditions, such as rheumatoid arthritis; however, they are not without side effects, potentially including lymphoma. We compared Epstein-Barr virus (EBV) levels in patients with inflammatory arthritis taking biologic agents and controls matched for disease, age, gender and disease duration who were biologic naïve. Secondly, we determined the risk of reactivation of EBV in patients taking biologics. METHODS: One hundred and twenty-two patients were recruited and blood samples were collected. Immunoglobulin G (IgG) antibody to EBV was analysed using enzyme-linked immunosorbent assay. EBV DNA was analysed using polymerase chain reaction (PCR) on all positive IgG samples. Quantitative measures of viral DNA were made and expressed as copies/reaction volume. Reactivation was defined as the presence of viral DNA in the plasma and PCR activity was evaluated between 6 and 18 months after anti-TNF therapy. RESULTS: IgG for EBV was detected in 98% of controls and 90% of cases. Viral reactivation related to EBV was not observed in this study. There was one patient who tested positive for EBV using PCR, but upon confirmatory testing, this sample was actually negative. No samples were positive on PCR at the follow-up time points. CONCLUSION: There was a high rate of EBV IgG in the cases and controls in this study. Given the small sample size and timeframe for this study, treatment with anti-TNF agents does not seem to lead to EBV reactivation, and thus, this is likely not a mechanism for the development of lymphoma in patients taking biologics.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".