Network meta-analysis of direct-acting antivirals in combination with peginterferon–ribavirin for previously untreated patients with hepatitis C genotype 1 infection
Bibliographic record
Abstract
AIM: To conduct a network meta-analysis (NMA) to determine the comparative efficacy, as measured by sustained virological response (SVR), between boceprevir (BOC), telaprevir (TEL), faldaprevir (FAL), simeprevir (SIM) and sofosbuvir (SOF) in combination with peginterferon-ribavirin (PR) against a control of PR. DESIGN: A literature search was conducted to identify randomized controlled trials (RCTs) including adult patients with hepatitis C virus genotype 1 who were naive to any prior therapy. RCTs assessing standard duration therapy (SDT) or response-guided therapy (RGT) BOC, TEL, FAL, SIM or SOF in combination with PR against a control of PR were eligible for inclusion. All RCTs must have provided SVR at either 12 or 24 weeks post-therapy cessation. RESULTS: We included nine RCTs. All direct-acting antivirals (DAAs) were found to perform better than PR. Additionally, SDT FAL was found to be better than the 240 mg RGT FAL regimen with the PR lead-in. A sensitivity analysis excluding RCTs with only SVR at 12 weeks was consistent with the results of the primary analysis. A sensitivity analysis removing an RCT assessing SIM that reported SVR of >60% in the PR control group additionally found that RGT SIM was superior to the 240 mg RGT FAL regimen with the PR lead-in. DISCUSSION: Our analyses indicate that SDT and RGT regimens of DAAs plus PR do not differ greatly in terms of SVR among treatment-naive hepatitis C genotype 1 patients. More advanced Bayesian network meta-analyses are likely needed to incorporate a comprehensive evidence base, expanding beyond randomized clinical trials.
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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 | 0.023 | 0.044 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.050 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".