Treatment Outcomes of Treatment-Naïve Hepatitis C Patients Co-Infected with HIV: A Systematic Review and Meta-Analysis of Observational Cohorts
Bibliographic record
Abstract
INTRODUCTION: Co-infection with Hepatitis C (HCV) and HIV is common and HIV accelerates hepatic disease progression due to HCV. However, access to HCV treatment is limited and success rates are generally poor. METHODS: We conducted a systematic review and meta-analysis to assess HCV treatment outcomes in observational cohorts. Two databases (Medline and EMBASE) were searched using a compound search strategy for cohort studies reporting HCV treatment outcomes (as determined by a sustained virological response, SVR) in HIV-positive patients initiating HCV treatment for the first time. RESULTS: 40 studies were included for review, providing outcomes on 5339 patients from 17 countries. The pooled proportion of patients achieving SVR was 38%. Significantly poorer outcomes were observed for patients infected with HCV genotypes 1 or 4 (pooled SVR 24.5%), compared to genotypes 2 or 3 (pooled SVR 59.8%). The pooled proportion of patients who discontinued treatment due to drug toxicities (reported by 33 studies) was low, at 4.3% (3.3-5.3%). Defaulting from treatment, reported by 33 studies, was also low (5.1%, 3.5-6.6%), as was on-treatment mortality (35 studies, 0.1% (0-0.2%)). CONCLUSIONS: These results, reported under programmatic conditions, are comparable to those reported in randomised clinical trials, and show that although HCV treatment outcomes are generally poor in HIV co-infected patients, those infected with HCV genotypes 2 or 3 have outcomes comparable to HIV-negative patients.
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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 | 0.014 | 0.034 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.030 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".