Distribution of Hepatitis C Risk Factors and HCV Treatment Outcomes among Central Canadian Aboriginal
Bibliographic record
Abstract
Background. Aboriginal Canadians face many lifestyle risk factors for hepatitis C exposure. Methods. An analysis of Ottawa Hospital Viral Hepatitis Clinic (Ottawa, Canada) patients between January 2000 and August 2013 was performed. HCV infection risk factors and HCV treatment outcomes were assessed. Socioeconomic status markers were based on area-level indicators linked to postal codes using administrative databases. Results. 55 (2.8%) Aboriginal and 1923 (97.2%) non-Aboriginal patients were evaluated. Aboriginals were younger (45.6 versus 49.6 years, p < 0.01). The distribution of gender (63.6% versus 68.3% male), HIV coinfection (9.1% versus 8.1%), advanced fibrosis stage (29.2% versus 28.0%), and SVR (56.3% versus 58.9%) was similar between groups. Aboriginals had a higher number of HCV risk factors, (mean 4.2 versus 3.1, p < 0.001) with an odds ratio of 2.5 (95% confidence interval: 1.4-4.4) for having 4+ risk factors. This was not explained after adjustment for income, social deprivation, and poor housing. Aboriginal status was not related to SVR. Aboriginals interrupted therapy more often due to loss to follow-up, poor adherence, and substance abuse (25.0% versus 4.6%). Conclusion. Aboriginal Canadians have higher levels of HCV risk factors, even when adjusting for socioeconomic markers. Despite facing greater barriers to care, SVR rates were comparable with non-Aboriginals.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".