Bibliographic record
Abstract
Chronic infection with hepatitis C virus (HCV) is a leading cause of liver-related morbidity and mortality worldwide and predisposes to liver fibrosis and end-stage liver complications. Liver fibrosis is the excessive accumulation of extracellular matrix proteins, including collagen, and is considered as a wound healing response to chronic liver injury. Its staging is critical for the management and prognosis of chronic hepatitis C (CHC) patients, whose number is expected to rise over the next decades, posing a major health care challenge. This review provides a brief update on HCV epidemiology, summarizes basic mechanistic concepts of HCV-dependent liver fibrogenesis, and discusses methods for assessment of liver fibrosis that are routinely used in clinical practice. Liver biopsy was until recently considered as the gold standard to diagnose and stage liver fibrosis. However, its invasiveness and drawbacks led to the development of non-invasive methods, which include serum biomarkers, transient elastography and combination algorithms. Clinical studies with CHC patients demonstrated that non-invasive methods are in most cases accurate for diagnosis and for monitoring liver disease complications. Moreover, they have a high prognostic value and are cost-effective. Non-invasive methods for assessment of liver fibrosis are gradually being incorporated into new guidelines and are becoming standard of care, which significantly reduces the need for liver biopsy.
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 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".