Prevalence and associated factors of hepatitis C infection (HCV) in a multi-site Canadian population of illicit opioid and other drug users
Bibliographic record
Abstract
Background: Hepatitis C virus (HCV) infection is highly prevalent in illicit drug user populations, with three in four new HCV infections related to this risk behaviour and a growing HCV disease burden in Canada. Using data from a multi-site cohort study of illicit opioid users in five Canadian cities (OPICAN), this paper explores the prevalence and predictors of HCV status in this high-risk population. Methods: HCV status of cohort participants was assessed by salivary antibody test. Univariate relationships of HCV status with select variables were examined on the basis of cohort baseline data, and subsequently multivariate models using logistic regression to determine independent predictors of HCV status were generated. Results: 54.6 % of the analysis sample (n=482) was HCV positive. Significant differences in terms of HCV prevalence existed across the sites. Significant variables in the final stepwise logistic regression model included age, site (Toronto), unprotected sex, injecting drug use, drug treatment and incarceration in past year, in addition to opioid use in combination with non-opioids.
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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 | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".