Real-world effectiveness of peginterferon α-2b plus ribavirin in a Canadian cohort of treatment-naïve chronic hepatitis C patients with genotypes 2 or 3: results of the PoWer and RediPEN studies
Bibliographic record
Abstract
The purpose of this investigation was to assess the real-life effectiveness of pegylated interferon (peg-IFN) α-2b with ribavirin (RBV) in a cohort of treatment-naïve patients with chronic genotypes 2 (G2) or 3 (G3) hepatitis C virus (HCV) infection. A post-hoc pooled analysis of two Canadian multicenter, observational studies, RediPEN and PoWer, was carried out. A total of 1242 G2- or G3-infected patients were included. The primary outcome was sustained virologic response (SVR). Secondary endpoints included early virologic response (EVR), end-of-treatment (EOT) response, and relapse. Multivariate logistic regression was used to identify independent predictors of treatment response. SVR in G2 and G3 was 74.4 % and 63.6 %, respectively. Relapse occurred in 12.7 % and 19.1 % of G2- and G3-infected patients achieving EOT response, respectively. Overall, G3 was found to independently predict reduced SVR [odds ratio (OR) = 0.20; p = 0.007] and increased relapse (OR = 6.84; p = 0.022). Among G3-infected patients, increasing fibrosis score was the most important factor predicting reduced SVR [F2 vs. F0/F1 (OR = 0.41; p = 0.009); F3 vs. F0/F1 (OR = 0.72; p = 0.338); F4 vs. F0/F1 (OR = 0.27; p = 0.001)]. Male gender (OR = 13.16; p = 0.020) and higher fibrosis score [F2 vs. F0/F1 (OR = 9.72; p = 0.016); F3/F4 vs. F0/F1 (OR = 4.23; p = 0.113)] were associated with increased relapse in G3 patients. These results support the real-life effectiveness of peg-IFN α-2b plus ribavirin in HCV G2- and G3-infected patients. Overall, genotype was identified as the most significant predictor of treatment outcome. Fibrosis score and gender were key outcome predictors in the G3-infected population. In clinical settings, peg-INF/RBV offers an alternative for patients without access to all oral direct-acting antivirals.
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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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".