Hepatitis B reactivation in HBsAg‐negative/HBcAb‐positive patients receiving rituximab for lymphoma: a meta‐analysis
Why this work is in the frame
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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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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.005 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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 it