Hepatitis B virus and hepatitis C virus treatment and management in patients receiving immune-modifying agents
Bibliographic record
Abstract
PURPOSE OF REVIEW: To increase awareness and review the management of chronic viral hepatitis in individuals treated with immune-modifying agents to avoid potentially severe consequences. RECENT FINDINGS: Hepatitis B virus (HBV) reactivation has been reported with a wide variety of immunosuppressive regimens ranging from corticosteroids to cytotoxic chemotherapy. In the rheumatology field, reactivation is best studied with anti-tumor necrosis factor-alpha agents and may occur even in individuals with 'resolved' HBV infection. These complications can be prevented with the use of well tolerated pre-emptive antiviral agents. Treatment of reactivation after it occurs is much less effective. Unlike HBV, acute deterioration is rare with immunosuppression in patients with hepatitis C virus (HCV) and prophylactic therapy is not indicated in these patients. However, patients should undergo evaluation for staging of liver disease preferably before immunosuppression because of the risk of drug-induced liver injury and also rheumatological complications, such as cryoglobulinemia. SUMMARY: HBV and HCV remain enormous global health problems with over 500 million people infected worldwide. Neither virus is cytopathic with liver damage and control of viral replication caused by the host immune response. With the increasing number and types of immunomodulatory therapies, HBV reactivation is becoming an increasingly recognized issue in many areas of medicine, particularly rheumatology. Unfortunately, screening rates are low, partially because of unclear clinical guidelines. HCV may also complicate immunomodulatory therapy, particularly if cirrhosis is present. The management of rheumatology patients with HBV and HCV infection is discussed with a focus on whom to screen and whom to treat to prevent consequences of these often unrecognized conditions.
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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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 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".