Pegylated interferon alpha 2b and ribavirin in HIV/hepatitis C virus-co-infected non-responders and relapsers to IFN-based therapy
Bibliographic record
Abstract
BACKGROUND: Pegylated interferon alpha (PEG-IFN-alpha) and ribavirin is the most effective available treatment for chronic hepatitis C virus (HCV) infection. Its role in HIV/HCV-co-infected patients who have failed IFN-based therapy is unclear. OBJECTIVE: The aim of this study was to determine the safety and efficacy of this therapy in HIV/HCV-co-infected non-responders and relapsers. DESIGN: An open-label cohort study of 32 non-responders and relapsers to IFN (with or without ribavirin). Patients were treated for 48 weeks with PEG-IFN-alpha 2b and ribavirin. MAIN OUTCOME MEASURE: A sustained virological response (SVR) defined as a negative HCV-RNA level 24 weeks after the end of treatment. RESULTS: The mean age of the patients was 40 years; 78% were men, 67% had genotype 1, and 36% had bridging fibrosis or cirrhosis. The majority had a CD4 cell count greater than 200 cells/microl (97%) and an undetectable HIV-RNA level (81%). Fifteen patients (47%) withdrew because of adverse events, predominantly neuropsychiatric. In an intention-to-treat analysis, a SVR was observed in five patients (16%); 9% with genotype 1 versus 29% with genotype 3 and 33% with genotype 4 (P = NS). Additional, but statistically non-significant, univariate predictors of response were lower serum HCV-RNA (P = 0.07) and higher alanine aminotransferase levels (P = 0.055) at baseline. No patient with bridging fibrosis or cirrhosis responded. Treatment had a minimal impact on HIV parameters. CONCLUSION: PEG-IFN-alpha 2b and ribavirin is a potentially useful therapy in HIV/HCV-co-infected patients who have failed standard IFN-based regimens. Strategies to improve adherence are vital so as to maximize long-term response rates.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".