Bibliographic record
Abstract
Chronic hepatitis C (CHC) is a major health problem worldwide, with approximately 200 million affected individuals and a significant rate of progression to end-stage cirrhosis and hepatocellular carcinoma (HCC). If hepatitis C virus (HCV) infection is left untreated in the population, then the number of liver-related deaths will soon double and the need for liver transplantation may increase to five times that seen today. Available therapies for CHC are restricted to interferon alpha (IFN alpha) monotherapy and to the combination of IFN alpha and ribavirin. Despite their high cost and side effects, both of these therapies have proved to be cost effective, particularly combination therapy. IFN alpha monotherapy for one year can induce sustained response (SR) rates of approximately 10% in naive patients infected with HCV genotype 1, and above 50% in those infected with other genotypes. Combination therapy can double or even triple the rate of SR in genotype 1 infections and may further increase the SR rate in the other HCV genotypes. Combination therapy has also been proven to be effective in approximately 50% of relapsed responders to IFN alpha monotherapy. In clinical practice, the decision to treat should be individualized and tailored on the basis of several virus- and host-related factors, particularly the grade and stage of liver disease, HCV genotype and levels of viremia. Appropriate monitoring of therapy by careful clinical evaluation, liver biochemistry and serum HCV RNA testing is mandatory. IFN alpha therapy may also prove to be effective in reducing the rate of HCC development in CHC regardless of whether a virological response is achieved, but this remains to be established.
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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.014 | 0.009 |
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".