Bibliographic record
Abstract
Hepatitis C has been identified as the most common cause of post-transfusion hepatitis worldwide, accounting for approximately 90% of this disease in Japan, the United States and Western Europe. Hepatitis C is a major global public health problem. New infections continue to occur, and the source of infection includes transfusion of blood or blood products from unscreened donors; transfusion of blood products that have not undergone viral inactivation; parenteral exposure to blood through use of contaminated and inadequately sterilized instruments and needles used in medical, dental and 'traditional' medicine; procedures such as hemodialysis; high risk sexual practices; household or sexual contacts of hepatitis C virus (HCV)-infected persons; and infants of HCV-infected mothers. In many countries, the relative contribution of the various sources of infection has not been defined with population-based epidemiological studies. Such studies are necessary to enable countries to prioritize their preventive measures and to make the most appropriate use of available resources. Given the substantial morbidity and mortality attributable to HCV-related chronic liver disease, each country, irrespective of economic status, should develop a plan of HCV-related public health activities for the prevention of new HCV infections and the treatment of established chronic infections.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.038 | 0.033 |
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".