Historical epidemiology of hepatitis C virus (<scp>HCV</scp>) in selected countries
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Chronic infection with hepatitis C virus (HCV) is a leading indicator for liver disease. New treatment options are becoming available, and there is a need to characterize the epidemiology and disease burden of HCV. Data for prevalence, viremia, genotype, diagnosis and treatment were obtained through literature searches and expert consensus for 16 countries. For some countries, data from centralized registries were used to estimate diagnosis and treatment rates. Data for the number of liver transplants and the proportion attributable to HCV were obtained from centralized databases. Viremic prevalence estimates varied widely between countries, ranging from 0.3% in Austria, England and Germany to 8.5% in Egypt. The largest viremic populations were in Egypt, with 6,358,000 cases in 2008 and Brazil with 2,106,000 cases in 2007. The age distribution of cases differed between countries. In most countries, prevalence rates were higher among males, reflecting higher rates of injection drug use. Diagnosis, treatment and transplant levels also differed considerably between countries. Reliable estimates characterizing HCV-infected populations are critical for addressing HCV-related morbidity and mortality. There is a need to quantify the burden of chronic HCV infection at the national level.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it