Reactivation of Hepatitis B Viral Infection in Inactive HBsAg Carriers Following Anti-Tumor Necrosis Factor-α Therapy
Bibliographic record
Abstract
OBJECTIVE: To investigate whether anti-tumor necrosis factor-alpha (TNF-alpha) therapy can influence the reactivation of hepatitis B virus (HBV) infection in inactive HBsAg carriers. METHODS: The medical records of 103 patients [59 with ankylosing spondylitis (AS), 41 with rheumatoid arthritis (RA), 2 with juvenile RA, and 1 with psoriatic arthritis] who had been treated with anti-TNF-alpha therapy were reviewed retrospectively. Data on seropositivity of HBV, HBV load, and serum aminotransferases prior to and after initiation of anti-TNF-alpha therapy were obtained. RESULTS: Eight patients were inactive HBsAg carriers, and all of them had normal liver function and undetectable HBV load prior to anti-TNF-alpha therapy. Reactivation of hepatitis B occurred in 1 patient during the course of anti-TNF-alpha therapy. After the third infusion of infliximab 5 mg/kg at Week 6, a blood test showed that the patient had normal liver function. When the patient returned for the fourth infusion of infliximab at Week 14, a blood test showed markedly elevated aspartate aminotransferase (AST)/alanine aminotransferase (ALT) levels (457 and 1054 IU/l, respectively) and increased viral DNA by HBV polymerase chain reaction (PCR). The fourth infliximab infusion was canceled, and entecavir 0.5 mg/day was prescribed. Then AST/ALT levels began to decrease and returned to normal range after 3 months. Followup HBV PCR showed negative results. CONCLUSION: We found 1 HBV reactivation case among 8 inactive HBsAg carriers following anti-TNF-alpha therapy. This finding supports the prophylactic use of antiviral agents in HBV carriers, even if they have normal liver function or an undetectable viral load.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".