Response to treatment following recently acquired hepatitis C virus infection in a multicentre collaborative cohort
Bibliographic record
Abstract
Pegylated interferon therapy is highly effective in recently acquired HCV. The optimal timing of treatment, regimen and influence of host factors remains unclear. We aimed to measure sustained virological response (SVR) during recent HCV infection and identify predictors of response. Data were from five prospective cohorts of high-risk individuals in Australia, Canada, Germany and the United States. Individuals with acute or early chronic HCV who commenced pegylated interferon therapy were included. The main outcome was SVR, and predictors were assessed using logistic regression. Among 516 with documented recent HCV infection, 237 were treated (pegylated interferon n = 161; pegylated interferon/ribavirin n = 76) (30% female, median age 35 years, 56% ever injected drugs, median duration of infection 6.2 months). Sixteen per cent (n = 38) were HIV/HCV co-infected. SVR among those with HCV mono-infection was 64% by intention to treat; SVR was 68% among HCV/HIV co-infection. Independent predictors of SVR in HCV mono-infection were duration of HCV infection (the odds of SVR declined by 8% per month of infection, aOR 0.92, 95% CI 0.85-0.99, P = 0.033), IFNL4 genotype (adjusted OR 2.27, 95% CI 1.13-4.56, P = 0.021), baseline HCV RNA <400 000 IU/mL (aOR 2.06, 95% CI 1.03-4.12, P = 0.041) and age ≥40 years (vs <30: aOR 2.92, 95% CI 1.31-6.49, P = 0.009), with no difference by drug regimen, HCV genotype, symptomatic infection or gender. The effect of infection duration on odds of SVR was greater among genotype-1 infection. Interferon-based HCV treatment is highly effective in recent HCV infection. Duration of infection, IFNL4 genotype and baseline HCV RNA levels can predict virological response and may inform clinical decision-making.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".