Prevention and management of hepatitis B virus reactivation in patients with hematological malignancies in the targeted therapy era
Bibliographic record
Abstract
Hepatitis due to hepatitis B virus (HBV) reactivation can be serious and potentially fatal, but is preventable. HBV reactivation is most commonly reported in patients receiving chemotherapy, especially rituximab-containing therapy for hematological malignancies and those receiving stem cell transplantation. Patients with inactive and even resolved HBV infection still have persistence of HBV genomes in the liver. The expression of these silent genomes is controlled by the immune system. Suppression or ablation of immune cells, most importantly B cells, may lead to reactivation of seemingly resolved HBV infection. Thus, all patients with hematological malignancies receiving anticancer therapy should be screened for active or resolved HBV infection by blood tests for hepatitis B surface antigen (HBsAg) and antibody to hepatitis B core antigen. Patients found to be positive for HBsAg should be given prophylactic antiviral therapy. For patients with resolved HBV infection, there are two approaches. The first is pre-emptive therapy guided by serial HBV DNA monitoring, and treatment with antiviral therapy as soon as HBV DNA becomes detectable. The second approach is prophylactic antiviral therapy, particularly for patients receiving high-risk therapy, especially anti-CD20 monoclonal antibody or hematopoietic stem cell transplantation. Entecavir and tenofovir are the preferred antiviral choices. Many new effective therapies for hematological malignancies have been introduced in the past decade, for example, chimeric antigen receptor (CAR)-T cell therapy, novel monoclonal antibodies, bispecific antibody drug conjugates, and small molecule inhibitors, which may be associated with HBV reactivation. Although there is limited evidence to guide the optimal preventive measures, we recommend antiviral prophylaxis in HBsAg-positive patients receiving novel treatments, including Bruton's tyrosine kinase inhibitors, B-cell lymphoma 2 inhibitors, and CAR-T cell therapy. Further studies are needed to determine the risk of HBV reactivation with these agents and the best prophylactic strategy.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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".