A Focused Review on Recent Advances in the Diagnosis and Treatment of Viral Hepatitis
Bibliographic record
Abstract
The global burden of viral hepatitis remains substantial despite advances in antiviral therapy and effective vaccines. There are five hepatitis viruses (hepatitis A, B, C, D, and E). Mortality related to hepatitis B virus and hepatitis C virus infections is among the top four global infectious diseases, together with human immunodeficiency virus infection, malaria, and tuberculosis. Of those deaths, approximately 47% are attributable to hepatitis B virus, 48% to hepatitis C virus and the remainder to hepatitis A virus and hepatitis E virus. Ending hepatitis epidemics as a major public health threat is feasible with the tools and approaches currently available. Effective vaccines are available for preventing viral hepatitis A, B and E infections. New oral, well-tolerated treatment regimens for chronic hepatitis C patients can achieve cure rates of over 90%. Effective treatment is also available for people with chronic hepatitis B virus infection; although for most people such treatment needs to be long-term, and recent advanced aim at a "functional cure" of hepatitis B. In this review article, we discuss the most recent advances of the diagnosis and treatment of viral hepatitis.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".