Hepatitis C in 2020
Bibliographic record
Abstract
In 1989, a collaboration between the Centers for Disease Control (CDC) and a California biotechnology company identified the hepatitis C virus (HCV, formerly known as non-A, non-B hepatitis virus) as the causative agent in the epidemic of silent posttransfusion hepatitis resulting in cirrhosis. We now know that, the HCV genome is a 9.6 kb positive, single-stranded RNA. A single open reading frame encodes a 3011 amino acid residue polyprotein that undergoes proteolysis to yield 10 individual gene products, consisting of 3 structural proteins (core and envelope glycoproteins E1 and E2) and 7 nonstructural (NS) proteins (p7, NS2, NS3, NS4A, NS4B, NS5A, and NS5B), which participate in posttranslational proteolytic processing and replication of HCV genetic material. Less than 25 years later, a new class of medications, known as direct-acting antivirals (DAAs) which target these proteins, were introduced to treat HCV infection. These highly effective antiviral agents are now approved for use in children as young as 3 years of age and have demonstrated sustained virologic responses exceeding 90% in most genotypes. Although tremendous scientific progress has been made, the incidence of acute HCV infections has increased by 4-fold since 2005, compounded in the last decade by a surge in opioid and intravenous drug use. Unfortunately, awareness of this deadly hepatotropic virus among members of the lay public remains limited. Patient education, advocacy, and counseling must, therefore, complement the availability of curative treatments against HCV infection if this virus is to be eradicated.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.070 | 0.050 |
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".