Hepatitis B Virus Reactivation by Immunosuppressive Therapy in Patients with Autoimmune Diseases: Risk Analysis in Hepatitis B Surface Antigen-negative Cases
Bibliographic record
Abstract
OBJECTIVE: To evaluate the risk of reactivation of resolved hepatitis B virus (HBV) by immunosuppressive therapy in patients with autoimmune diseases. METHODS: Thirty-five patients with autoimmune diseases were included in our study; all were hepatitis B surface antigen (HBsAg)-negative and antibody against hepatitis B core antigen-positive. They were followed for 8-124 weeks and clinical outcomes were analyzed, including serum levels of HBV-DNA and aminotransferase every 4 weeks during their immunosuppressive therapy for underlying autoimmune diseases. If HBV-DNA was detected during the immunosuppressive therapy, HBsAg, antibody against HBsAg (anti-HBs), hepatitis B e antigen (HBeAg), and antibody against HBeAg were also monitored every 4 weeks. RESULTS: HBV-DNA was detected in 6 out of 35 patients. Anti-HBs titer was significantly lower in the patients in whom HBV-DNA was detected compared with the others at baseline: 2.83 (range 0.24-168.50) mIU/ml vs 99.94 (range 0.00-5342.98) mIU/ml, respectively (p = 0.036). Outcomes of the 6 patients with HBV reactivation were as follows: HBV-DNA turned negative in 2 patients without nucleic acid analog (NAA) and 1 with NAA; 2 died due to bacterial sepsis; and 1 died due to autoimmune hemolytic anemia. Significant elevation of aminotransferase was found in only 1 patient, but HBsAg converted to positive in 2 patients and HBeAg converted to positive in 1 patient. CONCLUSION: Reactivation of resolved HBV can occur during standard immunosuppressive therapy for autoimmune diseases. The low titer of baseline anti-HBs may carry its risk.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".