The 11th Canadian Symposium on Hepatitis C Virus: ‘Getting back on track towards hepatitis C elimination’
Bibliographic record
Abstract
Hepatitis C virus (HCV) affects approximately 204,000 Canadians. Safe and effective direct-acting antiviral therapies have contributed to decreased rates of chronic HCV infection and increased treatment uptake in Canada, but major challenges for HCV elimination remain. The 11th Canadian Symposium on Hepatitis C Virus took place in Ottawa, Ontario on May 13, 2022 as a hybrid conference themed 'Getting back on track towards hepatitis C elimination.' It brought together research scientists, clinicians, community health workers, patient advocates, community members, and public health officials to discuss priorities for HCV elimination in the wake of the COVID-19 pandemic, which had devastating effects on HCV care in Canada, particularly on priority populations. Plenary sessions showcased topical research from prominent international and national researchers, complemented by select abstract presentations. This event was hosted by the Canadian Network on Hepatitis C (CanHepC), with support from the Public Health Agency of Canada and the Canadian Institutes of Health Research and in partnership with the Canadian Liver Meeting. CanHepC has an established record in HCV research and in advocacy activities to address improved diagnosis and treatment, and immediate and long-term needs of those affected by HCV infection. The Symposium addressed the remaining challenges and barriers to HCV elimination in priority populations and principles for meaningful engagement of Indigenous communities and individuals with living and lived experience in HCV research. It emphasized the need for disaggregated data and simplified pathways for creating and monitoring interventions for equitably achieving elimination targets.
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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.033 | 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".