Hepatitis C infection: its sequelae and outcomes--State-of-the-Art Workshop, September 24 to 25, 1998. Canadian Association for the Study of the Liver.
Bibliographic record
Abstract
This report summarizes a state-of-the-art workshop held in September 1998 on the "Natural History and Outcome of Hepatitis C Infection". Sixteen Canadian and two internationally renowned hepatologists were invited. A practical classification of HCV infection served as a framework for the meeting. The concepts of modelling of chronic disease, the epidemiology of HCV infection before the introduction of anti-HCV testing, and the outcome of various forms of chronic hepatitis C in adults and children were presented. Lectures on the outcome of HCV cirrhosis, hepatocellular carcinoma, the role of liver transplantation, the influence of host factors on outcome, iron overload in chronic hepatitis C and possible modification of the natural history by antiviral therapy were followed by discussion and consensus statements pertaining to each presentation. "The European Experience in Assessing Chronic Hepatitis C" was presented by Prof G Dusheiko from the United Kingdom, and Prof Leonard Seeff from the National Institutes of Health (United States) presented "The Epidemiology and Outcome of Hepatitis C Infection in the United States and the World".
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.042 | 0.010 |
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".