Bibliographic record
Abstract
In 2009, an association between the interleukin-28B (IL28B) polymorphism and treatment outcome for genotype 1 (G1) hepatitis C virus (HCV) infection, as well as spontaneous clearance of HCV, was reported. Since the initial publications, over 100 articles have appeared in the peer-reviewed literature, with many more manuscripts in press and abstracts presented at scientific meetings. Despite the proliferation of data concerning the IL28B polymorphism and HCV infection, there remain many critical unanswered questions about clinical implications and the underlying biological mechanisms. In this review, we discuss the basic principles of genome-wide association study methodologies that are important for interpreting the results of genetic association studies. We then review the current literature concerning the association between IL28B variants and interferon (IFN) treatment response in patients with chronic HCV infection, as well as spontaneous HCV clearance. We consider the relevance of the IL28B polymorphism to non-G1 HCV, as well as the special treatment populations of HIV/HCV co-infection and recurrent HCV post-liver transplantation. We review current knowledge of the biological mechanisms underlying this genetic association, including the link to liver IFN-stimulated gene expression, and identify continuing gaps in our knowledge and key research priorities. Finally, pegylated-IFN and ribavirin is no longer the standard of care for the treatment of G1 HCV, and we conclude by considering the relevance of IL28B polymorphisms in the era of 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".