Preventing hepatitis associated hepatocellular carcinoma through screening innovation and linkage to care (HbcCare)
Bibliographic record
Abstract
The Alberta Health funded Cancer Research for Screening and Prevention (CRSP) study is a comprehensive initiative addressing the increasing burden of hepatocellular carcinoma (HCC), a form of liver cancer, primarily driven by hepatitis B (HBV) and hepatitis C (HCV) viral infections. HCC incidence is rising in Canada, with HBV and HCV responsible for over 50% of cases. Despite the availability of effective treatments, gaps in testing and treatment persist, particularly among marginalized, rural, immigrant, refugee, and newcomer communities, in which chronic viral hepatitis has a higher prevalence, exacerbating the HCC burden. The CRSP project aims to bridge this gap by implementing community-centred strategies in Calgary. It seeks to provide low-barrier, accurate Dried Blood Spot (DBS) testing for HBV and HCV (by finger prick), streamline treatment referrals, and enroll individuals at risk into an established automatic-recall HCC ultrasound-based screening program. Key objectives of the CRSP study include establishing grassroots community-led screening programs, documenting, and evaluating patient experiences, linking infected individuals to established HCC screening and viral hepatitis treatment programs, and defining implementation requirements. The project's multifaceted approach encompasses close community engagement, knowledge translation activities, co-identifying research themes, participant recruitment, collaborative data analysis, and taking results directly back to the community. It emphasizes lasting partnerships aligned with community needs. The project, Preventing Hepatitis-Associated Hepatocellular Carcinoma through Screening Innovation and Linkage to Care unfolds in three linked phases: strengthening relationships with community organizations, linking HBV and HCV cases to care, and reducing disease burden. Expected outcomes include early detection, reduced HCC risk, improved access to treatment, high-prevalence area identification, and enhanced health outcomes.
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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 | 0.013 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".