Hepatitis B Virus Screening Before Chemotherapy for Lymphoma: A Cost-Effectiveness Analysis
Bibliographic record
Abstract
PURPOSE: Hepatitis B virus (HBV) reactivation is a potentially fatal complication of chemotherapy that can be largely prevented with antiviral prophylaxis. It remains unclear whether HBV screening is cost effective. METHODS: A decision model was developed to compare the clinical outcomes, costs, and cost effectiveness of three HBV screening strategies for patients with lymphoma before R-CHOP (rituximab plus cyclophosphamide, doxorubicin, vincristine, and prednisone) chemotherapy: screen all patients for hepatitis B surface antigen (HBsAg; Screen-All), screen patients identified as being at high risk for HBV infection (Screen-HR), and screen no one (Screen-None). Patients testing positive were administered antiviral therapy until 6 months after completion of chemotherapy. Those not screened were initiated on antiviral therapy only if HBV hepatitis occurred. Probabilities of HBV and lymphoma outcomes were derived from systematic literature review. A third-party payer perspective was adopted, costs were expressed in 2011 Canadian dollars, and a 1-year time horizon was used. RESULTS: Screen-All was the dominant strategy. It was least costly at $32,589, compared with $32,598 for Screen-HR and $32,657 for Screen-None. It was also associated with the highest 1-year survival rate at 84.99%, compared with 84.96% for Screen-HR and 84.86% for Screen-None. The analysis was sensitive to the prevalence of HBsAg positivity in the low-risk population, with Screen-HR becoming least costly when this value was ≤ 0.20%. CONCLUSION: In patients receiving R-CHOP for lymphoma, screening all patients for HBV reduces the rate of HBV reactivation (10-fold) and is less costly than screening only high-risk patients or screening no patients.
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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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.000 | 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 | 0.000 |
| 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".