Hepatitis B reactivation in HBsAg‐negative/HBcAb‐positive patients receiving rituximab for lymphoma: a meta‐analysis
Bibliographic record
Abstract
Patients with chronic hepatitis B (HBsAg-positive) are at risk of viral reactivation if rituximab is administered without antiviral treatment, a potentially fatal complication of treatment. Patients with so-called 'resolved hepatitis B virus infection' (HBsAg-negative/cAb-positive) may also be at risk. We performed a systematic review of the English and Chinese language literature to estimate the risk of hepatitis B virus (HBV) reactivation in HBsAg-negative/cAb-positive patients receiving rituximab for lymphoma. A pooled risk estimate was calculated for HBV reactivation. The impact of HBsAb status and study design on reactivation rates was explored. Data from 578 patients in 15 studies were included. 'Clinical HBV reactivation', (ALT >3 × normal and either an increase in HBV DNA from baseline or HBsAg seroreversion), was estimated at 6.3% (I(2) = 63%, P = 0.006). Significant heterogeneity was detected. Reactivation rates were higher in prospective vs retrospective studies (14.2% vs 3.8%; OR = 4.39, 95% CI 0.83-23.28). Exploratory analyses found no effect of HBsAb status on reactivation risk (OR = 0.083; P = 0.151). Our meta-analysis confirms a measurable and potentially substantial risk of HBV reactivation in HBsAg-negative/cAb-positive patients exposed to rituximab. However, heterogeneity in the existing literature limits the generalizability of our findings. Large, prospective studies, with uniform definitions of HBV reactivation, are needed to clarify the risk of HBV reactivation in HBsAg-negative/cAb-positive patients.
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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 | 0.008 | 0.015 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.041 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".