Hepatitis B reactivation in HBsAg-/cAb+ patients receiving rituximab: A meta-analysis.
Bibliographic record
Abstract
6592 Background: Patients with hepatitis B virus (HBV) who are HBsAg+ are recognized to be at risk of HBV reactivation if rituximab is administered in the absence of antiviral treatment. Recently, it has been reported that patients with so-called “resolved HBV infection”(HBsAg-/cAb+) may also be at risk; however, the degree of risk is not known. Methods: We performed a systematic review of the English and Chinese language literature in Medline (1996 to July week 2 2012) and Embase (1996 to 2012 week 29) using the MeSH terms “lymphoma” and “hepatitis B”. Eligible studies were limited to those reporting primary data on HBV reactivation rates in HBsAg-/cAb+ patients receiving rituximab. We excluded case series with less than 5 patients. Pooled estimates were calculated for HBV reactivation and the impact of HBsAb status on HBV reactivation rate was explored. Results: Data from 445 patients in 12 studies were included. Using a standardized definition of HBV reactivation, (ALT >3 x upper limit of normal AND either an increase in HBV DNA from baseline OR HBsAg seroreversion), the pooled estimate for the risk of HBV reactivation in HBsAg-/cAb+ patients was 5.4% (I2 = 63%, P = 0.009). Significant heterogeneity was apparent. Exploratory analyses suggested that patients were less likely to reactivate if they were HBsAb+ (OR = 0.32; 95% CI 0.12-0.85, P = 0.0285). Conclusions: Our meta-analysis confirms that there is a measurable risk of HBV reactivation in HBsAg-/cAb+ patients exposed to rituximab HBsAb+ patients may be at lower risk than those who are HBsAb-. However, heterogeneity in the risk estimates limits their generalizability. Large prospective studies are needed to clarify the risk of HBV reactivation in HBsAg-/cAb+ patients and to inform decisions about best practice.
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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 | 0.010 | 0.014 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.016 | 0.049 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".