When to treat and the benefits of treating hepatitis C in patients with haemophilia
Bibliographic record
Abstract
Chronic hepatitis C (CHC), a curable infection, remains endemic worldwide. More than 90% of individuals with haemophilia have been infected with hepatitis C virus (HCV) mostly caused by transfusion with non-virucidally treated clotting factor concentrates. Relevant to haemophilics, the risk of cirrhosis with CHC infection is greatest in males, those who have been infected for a long time, consume alcohol regularly, and/or are co-infected with HIV. The cure rate, using the current standard therapy for CHC with pegylated-interferon-alpha given weekly and ribavirin daily, ranges from 43% to 65% in those infected with genotype 1 and 50-90% with genotype 2 and 3 infections. Eradication of hepatitis C in those co-infected with HIV is less in part because full dose therapy is poorly tolerated. Achieving a sustained virological response (SVR) prevents progression to cirrhosis and in those with established cirrhosis prevents liver failure, and reduces the risk if hepatocellular carcinoma, and the need for liver transplant. Novel treatment options now in development are predominantly focused on inhibitors of HCV-specific enzymes. The treatment paradigm for haemophilics infected with hepatitis C is that all should be assessed for treatment once a diagnosis of chronic hepatitis C is made in order to achieve the highest chance of an SVR, i.e. cure.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".