Viral Hepatitis in the Canadian Inuit and First Nations Populations
Bibliographic record
Abstract
OBJECTIVE: To review published prevalence data regarding hepatitis A (HAV), B (HBV) and C (HCV) in Canadian Inuit and First Nations populations. METHODS: PubMed database search and review of all papers describing data derived from seroepidemiological surveys. RESULTS: The prevalence of anti-HAV positivity in Canadian Inuit and First Nations populations reported to date is high (range 75% to 95%) and approximately three times that of non-Aboriginal Canadians residing in the same communities. Among the Canadian Inuit, the prevalence of HBV infection is approximately 5%, or 20 times that of non-Aboriginal Canadians, while the risk of exposure to HBV is 25%, or five times higher. Regarding the First Nations population, preliminary data suggest the prevalences of HBV infection (0.3% to 3%) and exposure (10% to 22%) are similar to rates in non-Aboriginals residing in the same regions and participating in similar high risk activities. Serological evidence of HCV infection (anti-HCV) is more common in the Canadian Inuit and First Nations (1% to 18%) than the remainder of the Canadian population (0.5% to 2%); however, viremia (HCV-RNA positivity) is less common (less than 5% versus 75% of anti-HCV positive individuals, respectively). CONCLUSIONS: Viral hepatitis is common in the Canadian Inuit and First Nations populations. In the absence of coexisting human immunodeficiency virus infection and alcohol abuse, the outcomes of HBV and HCV appear to be more benign than in non-Aboriginal Canadians.
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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.012 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".