Bibliographic record
Abstract
#### Summary points Chronic infection with the RNA flavivirus hepatitis C is a major cause of liver disease.1 The Department of Health estimates that in the United Kingdom, chronic infection is present in 200 000 people—of whom 50% are unaware that they carry the virus—with variations in prevalence between different groups (0.04% in blood donors, 1% in people attending genitourinary clinics, and up to 50% in intravenous drug users). A general practitioner with an average list of 1800 can expect to have eight to 20 patients with hepatitis C infection. If such patients are identified and treated, the virus can be eradicated in more than half of them. We outline this area of hepatology, highlighting risk factors for acquisition, groups to screen, and specialist management of patients with chronic infection. In the UK the main mode of acquisition is recreational intravenous drug use; in developing countries transfusion of blood products and exposure to unclean or unsterilised objects remains important —for example, during circumcision, scarification, and tattooing (box 1). Outcomes of infection are not uniform (fig 1⇓).2 Acute infection is usually unrecognised, and 60-85% of patients progress to chronic infection with persistent detection of hepatitis C virus RNA. Fig 1 Natural history of hepatitis C …
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.004 |
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".