Sorting out cirrhosis: mechanisms of non‐response to hepatitis C therapy
Bibliographic record
Abstract
Although cirrhosis has long been recognized as an important negative predictor of treatment response for hepatitis C virus (HCV) therapy, the mechanisms underlying this association remain relatively poorly understood. Treatment has progressed rapidly with the introduction of highly effective all-oral therapies, with promising outcomes even in patients with advanced cirrhosis. However, even with the new therapies, it is clear that patients with cirrhosis require special attention. Efficacy continues to be somewhat reduced compared to non-cirrhotic patients and safety is an important concern. In this review, we explore the reasons for treatment non-response in patients with cirrhosis. We focus on how cirrhosis impacts on four important areas including drug delivery, drug uptake and metabolism, immune responses and drug toxicity with examples from the clinical and basic literature. Fortunately, as treatment continues to progress, many of the challenges of treating patients with cirrhosis will become less and less problematic.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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; both teacher heads agree on what is shown here.
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".