Gender Influences on Hepatitis C Incidence Among Street Youth in a Canadian Setting
Bibliographic record
Abstract
Purpose Few studies have examined gender-based differences in the risk of hepatitis C (HCV) infection among street-involved youth. We compared rates of HCV infection among male and female street-involved youth in a Canadian setting. Methods The At-Risk Youth Study (ARYS) is a prospective cohort of drug-using, street-involved youth. Study recruitment and follow-up occurred in Vancouver, Canada, between September 2005 and November 2011. Eligible participants were illicit drug-using youth aged 14–26 years at enrollment, recruited by street-based outreach. We evaluated rates of HCV antibody seroconversion, measured every six months during study follow-up, and used Cox proportional hazards regression to compare risk factors for HCV incidence between male and female street youth. Results Among 512 HCV-seronegative youth contributing 836 person-years of follow-up, 56 (10.9%) seroconverted to HCV. Among female participants, the incidence density of HCV infection was 10.9 per 100 person-years and in males 5.1 per 100 person-years (p = 0.009). In multivariate analyses, female gender was independently associated with a higher rate of HCV seroconversion (Adjusted Hazard Ratio (AHR) = 2.01; 95% Confidence Interval [CI], 1.18 – 3.44). Risk factors were similar in gender stratified analyses and included injection heroin and injection crystal methamphetamine, although syringe sharing was only associated with HCV incidence among males. Conclusions Among street-involved youth in this setting, females had double the incidence of HCV seroconversion demonstrating the need for gender focused HCV prevention interventions for this population.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| 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) | 0.004 | 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".