New hope for hepatitis C virus: Summary of global epidemiologic changes and novel innovations over 20 years
Bibliographic record
Abstract
Hepatitis C virus (HCV) is a global health concern associated with significant morbidity and mortality. Before the approval of second-generation direct-acting antiviral agents (DAAs), interferon therapy and liver transplantation constituted the mainstay of treatment. The introduction of well-tolerated oral DAAs in late 2013 has revolutionized HCV management with over 95% cure rates. The predominance of HCV-related liver transplantations has declined following the widespread approval of DAAs. Despite the unparallel efficacy observed among these novel therapies, pharmaceutical costs continue to limit equitable access to healthcare and likely contribute to the differential HCV infection rates observed globally. To reduce the burden of disease worldwide, essential agenda items for all countries must include the prioritization of integrated care models and access to DAAs therapies. Through transparent negotiations with the pharmaceutical industry, the consideration for compassionate release of medications to promote equitable division of care is paramount. Here we provide a literature review of HCV, changes in epidemiologic trends, access issues for current therapies, and global inequities in disease burden.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| 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".