Efficacy of antiviral therapy for hepatitis C after liver transplantation with cyclosporine and tacrolimus: A systematic review and meta-analysis
Bibliographic record
Abstract
Cyclosporine A (CSA), but not tacrolimus (TAC), inhibits hepatitis C virus (HCV) replication in vitro. Clinical reports on the efficacy of interferon-α (IFNα)-based antiviral therapy (AVT) for recurrent HCV after liver transplantation (LT) with CSA and TAC are conflicting. Our aim was to assess whether AVT for recurrent HCV after LT is more effective with CSA or TAC. We performed an electronic database search (1995-2012) and a manual abstract search (2005-2012). The a priori defined eligibility criteria included the use of AVT for recurrent HCV with IFN (standard or pegylated) and ribavirin and the reporting of sustained virological response (SVR) rates with CSA and TAC (the primary outcome). Two authors identified and extracted data independently. Dichotomous data were expressed as relative risks (RRs) and 95% confidence intervals (CIs) with a random effects model. In all, 5058 references were retrieved, and 1 randomized controlled trial (RCT) and 17 observational studies (13 full-text articles) met the eligibility criteria; the meta-analysis was based on the latter studies. The pooled SVR rates were 42% (395/945) with CSA and 35% (471/1364) with TAC (RR = 1.18, 95% CI = 1.00-1.39, P = 0.05). Although the pooled data contained significant heterogeneity (I(2) = 45%, P = 0.02), the SVR rates in the RCT were comparable (39% with CSA and 35% with TAC). Limiting the analysis to the 7 studies reporting on 40 or more patients in each group (with 1634 patients in all) favored CSA (RR = 1.23, 95% CI = 1.09-1.38, P < 0.001), and heterogeneity disappeared (I(2) = 0%, P = 0.62). In conclusion, IFN-based AVT for recurrent HCV after LT seems marginally more effective with CSA versus TAC; the study heterogeneity, however, limits firm conclusions.
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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 | 0.012 | 0.025 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.020 | 0.036 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".