Birth cohort hepatitis C antibody prevalence in real-world screening settings in Ontario
Bibliographic record
Abstract
Background: Widespread screening and treatment of hepatitis C virus (HCV) is required to decrease late-stage liver disease and liver cancer. Clinical practice guidelines and Canadian Task Force on Preventative Health Care recommendations differ on the value of one-time birth cohort (1945-75) HCV screening in Canada. To assess the utility of this approach, we conducted a real-world analysis of HCV antibody (Ab) prevalence among birth cohort individuals seen in different clinical contexts. Methods: Cross-sectional study of individuals born between 1945 and 1975 who completed HCV Ab testing at multiple participating centres in Ontario, Canada between January 2016 and December 2020. Differences in prevalence were compared by year of birth, gender, and setting. Results: Among 16,672 birth cohort individuals tested, HCV Ab prevalence was 3.2%. Prevalence was higher among younger individuals which increased from 0.9% among those born between 1945 and 1956 to 4.6% among those born between 1966 and 1975. Prevalence was higher among males (4.4%) compared with females (2.0%) and differed by test site. In primary care, the prevalence was 0.5%, whereas the prevalence was highest among those tested at drug treatment centres (28.7%) and through community outreach (14.0%). Conclusions: HCV Ab prevalence remains high in the 1945-1975 birth cohort. These data highlight the need to re-evaluate existing Canadian Preventative Task Force recommendations, to consider incorporating one-time birth cohort and/or other population-based approaches to HCV screening into the clinical workflow as a preventative health measure, and to increase training among community providers to screen for and treat HCV.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".