Eradication of Hepatitis C Infection: The Importance of Targeting People Who Inject Drugs
Bibliographic record
Abstract
Hepatitis C virus (HCV) affects ~170 million people worldwide and causes significant morbidity and mortality.1 In high-income countries, people who inject drugs (PWID) are at greatest risk of HCV infection.2 Until recently HCV eradication seemed unlikely, but recent advances in HCV treatment and improved understanding of the effectiveness of harm-reduction intervention effectiveness give reason for optimism. Current HCV treatments can cure ~75% of patients and new drugs will further improve effectiveness (over 90% cure) and improve tolerability.3 If HCV treatment can be delivered effectively to those at highest risk of onward transmission, significant reductions in future HCV cases are possible. The feasibility of disease eradication must be assessed on both scientific criteria (e.g., epidemiological susceptibility, effective and practical intervention available, and demonstrated feasibility of elimination) and political criteria (e.g., burden of disease, cost of intervention).4 With effective, curative treatment now available, HCV meets these criteria.
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.018 | 0.004 |
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".