Treatment of Hepatitis C in HIV-Coinfected Patients
Bibliographic record
Abstract
OBJECTIVE: To review the current management of hepatitis C virus (HCV) in persons coinfected with HIV. DATA SOURCES: A MEDLINE search (1966-February 2006) was conducted, using key words such as HIV, human immunodeficiency virus, hepatitis C, interferon, pegylated interferon, and therapy. Article bibliographies and conference abstracts were also reviewed to identify relevant studies. STUDY SELECTION AND DATA EXTRACTION: Studies that examined HCV treatment in individuals coinfected with HIV and articles that focused on HCV/HIV coinfection were considered for this review. DATA SYNTHESIS: Coinfection with HIV leads to a more rapid and severe course of HCV-related liver disease. Treatment of HCV with pegylated interferon (PEG-IFN) and ribavirin therapy is relatively well tolerated in individuals coinfected with HIV, with overall sustained virologic response (SVR) rates of 27-40%. High relapse rates and poor response in HCV-genotype 1 contribute to the lower SVR in coinfected individuals compared with HCV monoinfection. Treatment of HCV is more complicated in HIV-infected persons due to increased risk of myelosuppression, drug interactions, hepatotoxicity of antiretroviral therapy, and the relative contraindication to interferon therapy in advanced HIV disease. Current guidelines recommend that all HIV-positive patients with chronic HCV infection be considered as treatment candidates for anti-HCV therapy due to the higher risk of liver disease progression. Further studies are needed, however, to define the appropriate dose and duration of therapy in HCV/HIV-coinfected individuals. CONCLUSIONS: Response to treatment with PEG-IFN and ribavirin is poorer in patients coinfected with HCV/HIV than in those infected with HCV alone. The benefits of anti-HCV therapy, including viral eradication, need to be weighed against the risks of adverse effects and drug-drug interactions between anti-HCV and antiretroviral medications.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".