Risk and prophylaxis strategy of hepatitis B virus reactivation in patients with lymphoma undergoing chemotherapy with or without rituximab
Bibliographic record
Abstract
Hepatitis B virus (HBV) reactivation is a serious but preventable complication for patients with lymphoma receiving systemic therapy. Without antiviral prophylaxis, the HBV reactivation rate is estimated to be > 50% in patients who are positive for hepatitis B surface antigen (HBsAg), and fatal hepatic failure is not uncommon. Current guidelines suggest that routine antiviral prophylaxis should be administered to all HBsAg-positive patients until 6-12 months after completion of chemotherapy. For those who are negative for HBsAg and positive for hepatitis B core antibody, HBV reactivation is uncommon when a conventional dose of chemotherapy is administered. However, with rituximab-containing immunochemotherapy, the HBV reactivation rate is 18% and the clinical course can vary from asymptomatic viremia to fulminant hepatic failure that can be potentially fatal. In this review, we discuss the risk, clinical course and prophylactic strategy of HBV reactivation in patients with lymphoma treated with chemotherapy with or without rituximab.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".