The 5th Canadian Symposium on Hepatitis C Virus: We Are Not Done Yet—Remaining Challenges in Hepatitis C
Bibliographic record
Abstract
Hepatitis C virus (HCV) affects approximately 268,000 Canadians and results in more years of life lost than any other infectious disease in the country. Both the Canadian Institutes of Health Research (CIHR) and the Public Health Agency of Canada (PHAC) have identified HCV-related liver disease as a priority and supported the establishment of a National Hepatitis C Research Network. In 2015, the introduction of new interferon- (IFN-) free therapies with high cure rates (>90%) and few side effects revolutionized HCV therapy. However, a considerable proportion of the population remains undiagnosed and treatment uptake remains low in Canada due to financial, geographical, cultural, and social barriers. Comprehensive prevention strategies, including enhanced harm reduction, broader screening, widespread treatment, and vaccine development, are far from being realized. The theme of the 2016 symposium, "We're not done yet: remaining challenges in Hepatitis C," was focused on identifying strategies to enhance prevention, diagnosis, and treatment of HCV to reduce disease burden and ultimately eliminate HCV in Canada.
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 | 0.008 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.009 | 0.017 |
| Insufficient payload (model declined to judge) | 0.018 | 0.004 |
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".