Highlights of the Fourth Canadian Symposium on Hepatitis C: Moving towards a National Action Plan
Bibliographic record
Abstract
Hepatitis C virus (HCV) affects at least 268,000 Canadians and causes greater disease burden than any other infectious disease in the country. The Canadian Institutes of Health Research (CIHR) and the Public Health Agency of Canada (PHAC) have identified HCV-related liver disease as a priority. In 2015, the release of well-tolerated, short course treatments (~12 weeks) able to cure the majority of treated HCV patients revolutionized HCV therapy. However, treatment is extremely costly and puts a significant burden on the Canadian healthcare system. Thus, managing treatment costs and improving treatment engagement in those most in need will be a key challenge. Diagnosis and treatment uptake are currently poor in Canada due to financial, geographical, cultural, and social barriers. The United States, Australia, and Scotland all have National Action Plans to prevent, diagnose, and treat HCV in order to efficiently reduce the burden and costs associated with HCV-related liver disease. The theme of the 4th annual symposium held on Feb 27, 2015, "Strategies to Manage HCV Infection in Canada: Moving towards a National Action Plan," was aimed at identifying strategies to maximize the impact of highly effective therapies to reduce HCV 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 | 0.012 | 0.014 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.013 | 0.018 |
| Insufficient payload (model declined to judge) | 0.027 | 0.006 |
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".