Global cascade of care for chronic hepatitis C virus infection: A systematic review and meta‐analysis
Bibliographic record
Abstract
The World Health Organization 2030 targets for hepatitis C virus (HCV) elimination include diagnosing 90% of people with HCV and treating 80% of people diagnosed with HCV. This systematic review assessed reported data on the HCV care cascade in various countries and populations, with a focus on direct-acting antiviral (DAA) treatment uptake. Bibliographic databases and conference presentations were searched for studies reporting the HCV care cascade (DAA treatment uptake was a requirement) among the overall population with HCV or sub-populations at greater risk of HCV. Population-based studies, with participants representative of a city, province/state or country were eligible. Twenty eligible studies were included, reporting HCV care cascade in 28 populations/sub-populations from 11 countries. DAA treatment uptake at national levels was reported from Iceland (95%), Egypt (92%), Georgia (79%), Norway (18%) and Sweden (8%), and at sub-national levels from the Netherlands (52%), Canada (50%), the United States (29%) and Denmark (5%). Among people with HIV-HCV co-infection, DAA treatment uptake was 62% in Canada, 44% in the Netherlands, 21% in Switzerland and 18% in the United States. Among people who inject drugs, DAA treatment uptake was 50% in Georgia, 40% in Canada, 37% in Australia and 13% in the United States. Data among people experiencing homelessness were only available from the United States (treatment uptake: 12%-14%). We found no eligible study reporting HCV care cascade data in prisons. Relatively few countries reported HCV care cascade at the national level. DAA treatment uptake was widely varied across populations/sub-populations, with higher rates reported in recent years.
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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 | 0.012 | 0.032 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.034 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".