Screening for hepatitis B virus (HBV) prior to chemotherapy: A cost-effectiveness analysis.
Bibliographic record
Abstract
6059 Background: Hepatitis B virus (HBV) reactivation during chemotherapy may lead to disruption of chemotherapy, hospitalization for hepatitis, or even death. Prophylactic therapy with lamivudine (LAM) has been shown to prevent HBV reactivation. However, screening for HBV is uncommon in practice, and it remains unclear whether its benefits will outweigh its costs and inconvenience. Methods: A decision analytic model was developed for patients with lymphoma to compare the clinical outcomes and costs over a 1-year horizon of 3 strategies for HBV screening prior to R-CHOP chemotherapy: (1) screen all patients for HBsAg (All), (2) screen patients identified as being high risk for HBV infection (HR), and (3) screen no one (None). Patients testing positive were given LAM until 6 months following completion of chemotherapy. Those not screened were initiated on LAM only when HBV hepatitis occurred. Risks for HBV hepatitis, recovery, HBV-related death, and lymphoma outcomes were derived from systematic literature review from 1997-2009. A third-party direct payer perspective in 2010 Canadian dollars was used. Results: See table. Screening all patients was the dominant strategy. It was both least costly and most effective in increasing the 1-year survival rate. This is likely because it reduced both hospitalization and death from HBV-related hepatitis, which are associated with considerable cost. The analysis was sensitive to the cost of HBsAg testing, the prevalence of HBsAg positivity in both the high risk and low risk groups, and the effectiveness of LAM in preventing reactivation. Conclusions: In patients receiving CHOP-R for lymphoma, screening all patients for HBV is more clinically effective and less costly than screening only high-risk patients or screening no patients. Further study is needed to examine the cost-effectiveness of screening of HBV in other settings. Outcome/strategy Screen all Screen HR Screen none Cost ($CDN 2010) $30,367 $30,371 $30,413 1-year survival rate (%) 0.8500 0.8497 0.8489 HBV hepatitis (per 1,000 patients) 0.4 1.8 6 HBV hepatitis requiring hospitalization (per 1,000 patients) 0.1 0.8 2.7 Hepatitis death (per 1,000 patients) 0 0.1 0.4
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 | 0.008 | 0.020 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.011 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".