Effect of peginterferon alfa-2a on liver histology in chronic hepatitis C: A meta-analysis of individual patient data
Bibliographic record
Abstract
Multicenter randomized trials have shown that once-weekly pegylated interferon (peginterferon) alfa-2a is more efficacious than conventional interferon alfa-2a (IFN) in patients with chronic hepatitis C. We performed a meta-analysis of 1,013 previously untreated patients (from 3 randomized trials) with pretreatment and post-treatment liver biopsies to assess the differences between peginterferon alfa-2a and IFN in terms of their effects on liver histology. Reported values were standardized mean differences (SMD) between patients receiving peginterferon alfa-2a and those receiving IFN (post-treatment value minus baseline value for each group). We used a random-effects model to quantify the average effect of peginterferon alfa-2a on liver histology. Peginterferon alfa-2a significantly reduced fibrosis compared with IFN (SMD, -0.14; 95% CI: -0.27, -0.01; P =.04). A reduction in fibrosis was observed among sustained virologic responders (SMD, -0.59; 95% CI: -0.89, -0.30; P <.0001) and patients with recurrent disease (SMD, -0.34; 95% CI: -0.54, -0.14; P =.0007), whereas no significant reduction was observed among nonresponders (SMD, -0.13; 95% CI: -0.32, 0.05; P =.15). Logistic regression analysis indicated that patients with sustained virologic responses (SVRs) had an odds ratio (OR) of 1.61 (95% CI: 1.14, 2.29) for reduction in fibrosis compared with patients without SVRs, whereas obese patients (body mass index [BMI] > 30 kg/m(2)) had an OR of 0.56 (95% CI: 0.35, 0.90) compared with normal-weight (BMI < 25 kg/m(2)) and overweight patients (BMI, 25-30 kg/m(2)). In conclusion, in patients with chronic hepatitis C with or without cirrhosis, peginterferon alfa-2a (relative to IFN) significantly reduced fibrosis. The beneficial effects of peginterferon on liver histology are closely related to virologic response.
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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 | 0.025 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.013 | 0.050 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| 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".