Natural history of hepatitis C virus infection in HIV-infected individuals and the impact of HIV in the era of highly active antiretroviral therapy: a meta-analysis
Bibliographic record
Abstract
OBJECTIVES: To estimate stage-specific transition probabilities in individuals coinfected with HIV and hepatitis C virus (HCV), to examine the effect of covariates on these rates, and to investigate the effect of HIV on HCV-related cirrhosis in the era of highly active antiretroviral therapy (HAART). DESIGN: Systematic review of natural history studies among HCV-infected individuals. METHODS: Markov maximum likelihood estimation method was used to estimate stage-specific transition probabilities. A meta-analysis was performed to obtain pooled transition probabilities, and a meta-regression to investigate the impact of covariates on these rates. Risk of cirrhosis between individuals monoinfected with HCV and coinfected with HIV/HCV were compared by HAART status. RESULTS: The estimated mean (95% confidence intervals) annual transition probabilities of 3567 individuals coinfected with HIV/HCV (n = 17 studies) were as follows: fibrosis stage (F) F0 --> F1 0.122 (0.098-0.153); F1 --> F2 0.115 (0.095-0.140); F2 --> F3 0.124 (0.097-0.159); and F3 --> F4 0.115 (0.098-0.135) units/year. The prevalence of cirrhosis after 20 and 30 years of HCV infection was 21% (16-28%) and 49% (40-59%), respectively. Longer duration of HCV infection was significantly associated with slower rate of fibrosis progression. The overall rate ratio of cirrhosis between individuals coinfected with HIV/HCV and monoinfected with HCV (n = 27 studies) was 2.1 (1.5-3.0), 2.5 (1.8-3.4) in the non-HAART group, and 1.7 (1.1-2.8) in the HAART group. CONCLUSION: The rate of fibrosis progression among individuals coinfected with HIV/HCV appears constant. Our results confirm that chronic hepatitis C outcomes are worse among coinfected individuals. Over the period studied, HAART did not appear to fully correct the adverse effect of HIV infection on HCV prognosis.
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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 | 0.013 | 0.025 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.049 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".