Hepatitis C in Canada and the importance of risk-based screening
Bibliographic record
Abstract
Chronic hepatitis C (CHC) remains a public health issue affecting an estimated 220,000 individuals in Canada. In 2011, approximately 44% of those with CHC were unaware of their infection. Hepatitis C is infectious in origin, and if left untreated, can lead to significant morbidity and mortality in its chronic form, including liver cirrhosis, hepatocellular carcinoma and liver failure. These health outcomes are associated with comorbidities, adding a burden to the Canadian health care system. Recent advancements in the treatment of hepatitis C have changed the clinical landscape. In Canada, the prevalence of incident cases is higher in specific population groups. Injection drug use (IDU) currently accounts for the highest proportion of new hepatitis C virus (HCV) infection. It is unclear to what extent HCV infection through health care or personal services use contributed to current prevalent cases of CHC. The Canadian Task Force on Preventive Health Care (CTFPHC) is currently reviewing the evidence for different approaches to HCV screening and the benefits and harms of screening. Risk-based screening remains critical to detecting hepatitis C as knowing one's status has been linked to the cascade of care and improved population health outcomes. This article intends to highlight risk factors associated with the acquisition of HCV so that health care providers can screen, where appropriate, and detect CHC.
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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".