The 8<sup>th</sup> Canadian Symposium on Hepatitis C virus: “Improving diagnosis and linkage to care”
Bibliographic record
Abstract
Hepatitis C virus (HCV) affects approximately 250,000 Canadians. Although safe and effective (>95% cure rates) antiviral therapies have become available within the past 5 years, chronic HCV infection still remains a major driver of end-stage liver disease and liver transplantation. Both the Canadian Institute for Health Research and the Public Health Agency of Canada recognize the impact of HCV-related liver diseases and support the Canadian Network for Hepatitis C (CanHepC), a National network for the scientific study of hepatitis C that organizes an annual symposium as part of its knowledge translation mandate. At the 8th Canadian Symposium on Hepatitis C Virus in May 2019, basic scientists, clinicians, epidemiologists, social scientists, and community members came together to share their work under the theme of “Improving diagnosis and linkage to care”. This symposium also marked the launch of the Blueprint to inform hepatitis C elimination efforts in Canada, a policy framework that outlines specific targets, suggested activities, and evidence-based best practices to guide provincial, territorial and federal organizations developing their own HCV elimination strategies.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.052 | 0.020 |
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".