Hepatitis B Virus Screening for Patients With Cancer Before Therapy: American Society of Clinical Oncology Provisional Clinical Opinion Update
Bibliographic record
Abstract
PURPOSE: This updated provisional clinical opinion presents a revised opinion based on American Society of Clinical Oncology panel consensus in the context of an evolving database. CONTEXT: Despite the 2010 provisional clinical opinion recommendation, there is still evidence of suboptimal hepatitis B virus (HBV) screening among patients at high risk for HBV infection or HBV reactivation after chemotherapy. This updated provisional clinical opinion introduces a risk-adaptive strategy to identify and treat patients with HBV infection to reduce their risk of HBV reactivation. PROVISIONAL CLINICAL OPINION: Medical providers should screen by testing patients for HBV infection before starting anti-CD20 therapy or hematopoietic cell transplantation. Providers should also screen patients with risk factors for HBV infection. Screening should include both hepatitis B surface antigen (HBsAg) and hepatitis B core antibody (anti-HBc), because reactivation can occur in patients who are HBsAg positive/anti-HBc positive or HBsAg negative/anti-HBc positive. Either total anti-HBc or anti-HBc immunoglobulin G (not immunoglobulin M) test should be used. Clinicians should start antiviral therapy for HBsAg-positive/anti-HBc-positive patients before or contemporaneously with cancer therapy and monitor HBsAg-negative/anti-HBc-positive patients for reactivation with HBV DNA and ALT levels, promptly starting antivirals if reactivation occurs. Clinicians can initiate antivirals for HBsAg-negative/anti-HBc-positive patients anticipating cancer therapies associated with a high risk of reactivation, or they can monitor HBV DNA and ALT levels and initiate on-demand antivirals. For patients who neither have HBV risk factors nor anticipate cancer therapy associated with a high risk of reactivation, current evidence does not support HBV screening before initiation of cancer therapy. Two panel members provided a minority viewpoint, involving a strategy of universal HBsAg and selective anti-HBc testing.
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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 | 0.009 | 0.062 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.008 | 0.009 |
| Insufficient payload (model declined to judge) | 0.017 | 0.013 |
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".