Bibliographic record
Abstract
INTRODUCTION: Management of hepatitis C virus (HCV) is rapidly changing as a result of new direct-acting antivirals (DAA). SOURCES OF DATA: Several peer-reviewed papers featuring new DAAs are now available. Additionally, as new data are emerging so quickly, we also reviewed recent presentations at international congresses, published in abstract form. AREAS OF AGREEMENT: New DAAs are efficacious and superior to prior treatment regimens, with minimal side effects. Shorter interferon-free regimens will soon be the mainstay of HCV treatment. AREAS OF CONTROVERSY: Access to new DAAs is variable across global regions. One approach to treating HCV may be to assess early viral kinetics of treatment to identify who may be cured with standard peg-interferon/ribavirin therapy as opposed to using a DAA in all patients. GROWING POINTS: Newer studies with combination of DAAs are being conducted. The ideal interferon-free regimen has yet to be determined. AREAS TIMELY FOR DEVELOPING RESEARCH: HCV genotype 3 is the new difficult-to-treat genotype. More efficacious regimens for treating HCV genotype 3 are needed. Subgroups of patients who only require even shorter regimens of 6-8 weeks need to be identified. There is still very little data on interferon-free regimens in patients with decompensated cirrhosis and certain other subgroups.
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 | 0.001 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 0.005 |
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".