The 7th Canadian Symposium on Hepatitis C Virus: “Toward Elimination of HCV: How to Get There”
Bibliographic record
Abstract
Hepatitis C virus (HCV) affects more than 268,000 people in Canada. Both the Canadian Institutes of Health Research and the Public Health Agency of Canada recognize the significant impact of HCV-related liver diseases and supported the establishment of a national hepatitis C research network, the Canadian Network on Hepatitis C (CanHepC). Interferon-free direct-acting antiviral regimens lead to more than 95% cure rates in almost all patients with well-tolerated short-course therapy. However, the goal of eliminating HCV in Canada cannot be fully realized until we overcome the financial, geographical, cultural, and social barriers that affect the entire continuum of care from diagnosis and linkage to care through treatment and prevention of new and reinfections. Current practices face difficulties in reversing HCV-induced immunological defects, expanding treatment to neglected communities, combating reinfections and co-infections, and expediting and simplifying the processes of diagnosis and treatment. As part of its knowledge translation mandate, CanHepC has organized the annual Canadian symposium on hepatitis C since 2012. The theme of this year's symposium, "Toward Elimination of HCV: How to Get There?" focused on identifying the requirements of our therapeutic strategies and health policies for the elimination of HCV in Canada.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.024 | 0.007 |
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".