6th Canadian Symposium on Hepatitis C Virus: Delivering a cure for hepatitis C infection—What are the remaining gaps?
Bibliographic record
Abstract
Estimates are that more than 250,000 people in Canada are chronically infected with hepatitis C virus (HCV), and many more are unaware of their infection status. If untreated, chronic HCV infection can lead to cirrhosis and subsequent complications such as hepatocellular carcinoma. The Canadian Network on Hepatitis C, supported by the Public Health Agency of Canada and the Canadian Institutes of Health Research, has been committed to the scientific study of chronic hepatitis C and to supporting the advocacy work to improve diagnosis and access to HCV care in Canada. Although the treatment of HCV infection has been greatly advanced with direct-acting antivirals, with cure rates as high as 95%, many challenges remain in the implementation of HCV care. These issues include the lack of an effective vaccine, infection screening, treatment failure or resistance, post-cure health issues, limitations of treatment access despite increased provincial subsidization, complex needs of at-risk populations (ie, injection drug users, societal obstacles). At the 6th Canadian Symposium on HCV in March 2017, the theme "Delivering a Cure for Hepatitis C Infection: What Are the Remaining Gaps?" provided a framework in which basic scientists, clinicians, epidemiologists, social scientists, and community members interested in HCV research in Canada could showcase how they are working to address these ongoing challenges.
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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.008 | 0.012 |
| Insufficient payload (model declined to judge) | 0.025 | 0.005 |
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".