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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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 teacher head, 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".