The 9th Canadian Symposium on Hepatitis C Virus: Advances in HCV research and treatment towards elimination
Bibliographic record
Abstract
Hepatitis C virus (HCV) elimination has evolved into a coordinated global effort. Canada, with more than 250,000 chronically infected individuals, is among the countries leading this effort. The 9th Canadian Symposium on HCV, held in February 2020, thus established and addressed its theme, 'advances in HCV research and treatment towards elimination', by gathering together basic scientists, clinicians, epidemiologists, social scientists, and community members interested in HCV research in Canada. Plenary sessions showcased topical research from prominent international and national researchers, complemented by select abstract presentations. This event was hosted by the Canadian Network on Hepatitis C (CanHepC), with support from the Public Health Agency of Canada and the Canadian Institutes of Health Research and in partnership with the Canadian Liver Meeting. CanHepC has an established record in HCV research by its members and in its advocacy activities to address the care, treatment, diagnosis, and immediate and long-term needs of those affected by HCV infection. Many challenges remain in tackling chronic HCV infection, such as the need for a vaccine; difficult-to-treat populations and unknown aspects of patient subgroups, including pregnant women and children; vulnerable people; and issues distinct to Indigenous peoples. There is also increasing concern about long-term clinical outcomes after successful treatment, with the rise in comorbidities such as diabetes, cardiovascular disease, and fatty liver disease and the remaining risk for hepatocellular carcinoma in cirrhotic individuals. The symposium addressed these topics in highlighting research advances that will collectively play an important role in eliminating HCV and minimizing subsequent health challenges.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.034 | 0.011 |
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".