The Second Canadian Symposium on Hepatitis C Virus: A Call to Action
Bibliographic record
Abstract
In Canada, hepatitis C virus (HCV) infection results in considerable morbidity, mortality and health-related costs. Within the next three to 10 years, it is expected that tolerable, short-duration (12 to 24 weeks) therapies capable of curing >90% of those who undergo treatment will be approved. Given that most of those already infected are aging and at risk for progressive liver disease, building research-based interdisciplinary prevention, care and treatment capacity is an urgent priority. In an effort to increase the dissemination of knowledge in Canada in this rapidly advancing field, the National CIHR Research Training Program in Hepatitis C (NCRTP-HepC) established an annual interdisciplinary Canadian Symposium on Hepatitis C Virus. The first symposium was held in Montreal, Quebec, in 2012, and the second symposium was held in Victoria, British Columbia, in 2013. The current article presents highlights from the 2013 meeting. It summarizes recent advances in HCV research in Canada and internationally, and presents the consensus of the meeting participants that Canada would benefit from having its own national HCV strategy to identify critical gaps in policies and programs to more effectively address the challenges of expanding HCV screening and treatment.
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 | 0.010 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.004 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.012 | 0.015 |
| Insufficient payload (model declined to judge) | 0.031 | 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".