Risk factors for hepatitis C virus infection among street youths.
Bibliographic record
Abstract
BACKGROUND: The relative contributions to risk of hepatitis C virus (HCV) infection resulting from unsafe sexual behaviours and exposures to blood (e.g., tattooing, body piercing and injection drug use) among youths at risk are not well known. We interviewed street youths about risk factors for HCV infection and documented their HCV antibody status. METHODS: From December 1995 to September 1996 we recruited 437 youths aged 14 to 25 years who met specific criteria for itinerancy. Data on sociodemographic characteristics and lifetime risk factors were obtained during a structured interview, and a venous blood sample was taken for HCV antibody testing. RESULTS: Many of the subjects reported behaviours that put them at risk for blood-borne diseases: 45.8% had injected drugs, 56.5% had at least 1 tattoo, and 78.3% had body piercing. The overall prevalence of HCV infection was 12.6% (95% confidence interval [CI] 9.7%-15.9%). In a multivariate logistic regression analysis, injecting drugs (adjusted odds ratio [OR] 28.4 [95% CI 6.6-121.4]), being over 18 years of age (adjusted OR 3.3 [95% CI 1.6-7.0]) and using crack cocaine (adjusted OR 2.3 [95% CI 1.0-5.3]) were independent risk factors for HCV infection. Having more than 1 tattoo (adjusted OR 1.8 [95% CI 0.95-3.6]) was marginally associated with HCV infection, and body piercing was not. INTERPRETATION: Drug injection was the factor most strongly associated with HCV infection among street youths. Given that injection drug users are the driving force of the HCV infection epidemic in Canada, increased intervention efforts to prevent initiation of drug injection are urgently needed to curb the epidemic.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".