Hepatitis C: from discovery to cure
Bibliographic record
Abstract
Background and Objectives With a prevalence of approximately 71 million infected individuals globally, chronic hepatitis C virus ( HCV ) infection is one of the major causes of chronic hepatitis leading to hepatocellular carcinoma ( HCC ). This review aimed to summarize the general history of HCV and provide a perspective on future journey of HCV . Materials and Methods We searched for articles published in periodicals, monographs and edited books with the key words including ‘non‐A, non‐B hepatitis’, ‘ HCV ’, ‘interferon’ and ‘direct‐antiviral agents ( DAA s)’. And the data emphasized HCV emergence, identification, treatment, epidemiology as well as the cellular and animal models leading to the eradication of HCV infections, were summarized. Results and Conclusion The battle against hepatitis C is destined to be recorded in history as one of science's remarkable success. Although the revolutionary direct‐antiviral agents ( DAA s) are able to cure more than 95% of HCV patients, access to diagnosis and therapy remains improved. More efforts should be made to promote HCV screening, treatment delivery and vaccine development.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".