Increased risk for hepatitis C associated with solvent use among Canadian Aboriginal injection drug users
Bibliographic record
Abstract
Background: Solvent abuse is a particularly serious issue affecting Aboriginal people. Here we examine the association between solvent use and socio-demographic variables, drug-related risk factors, and pathogen prevalence in Aboriginal injection drug users (IDU) in Manitoba, Canada. Methods: Data originated from a cross-sectional survey of IDU from December 2003 to September 2004. Associations between solvent use and variables of interest were assessed by multiple logistic regression. Results: A total of 266 Aboriginal IDU were included in the analysis of which 44 self-reported recent solvent use. Hepatitis C infection was 81% in solvent-users, compared to 55% in those reporting no solvent use. In multivariable models, solvent-users were younger and more likely to be infected with hepatitis C (AOR: 3.5; 95%CI: 1.3,14.7), to have shared needles in the last six months (AOR: 2.6; 95%CI:1.0,6.8), and to have injected talwin & Ritalin (AOR: 10.0; 95%CI: 3.8,26.3). Interpretation: High hepatitis C prevalence, even after controlling for risky injection practices, suggests that solvent users may form closed networks of higher risk even amongst an already high-risk IDU population. Understanding the social-epidemiological context of initiation and maintenance of solvent use is necessary to address the inherent inequalities encountered by this subpopulation of substance users, and may inform prevention strategies for other marginalized populations.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".