Endpoints of therapy in chronic hepatitis B #
Bibliographic record
Abstract
Because clearance of hepatitis B virus (HBV) infection is rarely, if ever, achievable, the goals of therapy necessarily focus on prevention of bad clinical outcomes. Ideally, therapies would be shown to prevent tangible clinical endpoints like development of cirrhosis, end-stage liver disease and hepatocellular carcinoma. However, these endpoints typically take years or decades to occur and are therefore impractical targets for clinical trials which last only 1-2 years. As a result, surrogate biomarkers that are believed to correlate with long-term outcome are used to evaluate therapy. Of the clinical, biochemical, serological, virological, and histological endpoints that have been evaluated, none has been shown to be ideal on its own. Symptoms are uncommon and aminotransferase levels fluctuate spontaneously. Loss of hepatitis B e antigen (HBeAg) has been the traditional therapeutic endpoint; however, the indefinite durability off treatment and the emergence of HBeAg-negative disease have made it inadequate as the sole goal of therapy. Loss of hepatitis B surface antigen is associated with improved clinical outcomes, but it is rarely achieved with current therapies. Suppression of viral replication, as measured by serum HBV DNA levels, has become the major goal of therapy, particularly if maintained off therapy. Although useful, the significance of viral levels depends on the stage of disease, degree of liver damage, and the type of therapy. Finally, liver biopsy, often considered the gold standard, is invasive, prone to sampling error, and may take years to change significantly.At present, there is no ideal biomarker for evaluation of therapies for hepatitis B. Future research should be directed at development and validation of surrogate markers that accurately predict or reflect clinically relevant outcomes of chronic hepatitis B.
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 | 0.017 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".