Sociodemographic risk factors for hepatitis C virus infection in a prospective cohort study of 257 persons in Canada who inject drugs
Bibliographic record
Abstract
BACKGROUND: Approximately 60% of incident hepatitis C virus (HCV) infections are due to intravenous drug use; therefore, understanding the socio-demographics of people who inject drugs (PWID) is necessary to achieve HCV elimination. METHODS: In this prospective cohort study of PWID, we determined patients’ baseline HCV antibody, hepatitis B virus (HBV), and HIV serological status. HCV antibody– negative (anti-HCV-negative) cases were followed for seroconversion (median 17 mo with q3m testing) as part of a larger study to develop a vaccine for HCV. An interviewer-administered baseline questionnaire completed with all patients evaluated socio-demographic and clinical characteristics. RESULTS: We tested 257 PWID (median age 40 [range 49–31]y, 81% men, 63% Caucasian, 28% Indigenous). Of these, 28% were positive for HCV antibodies (anti-HCV-positive) (median age 42 [range 49–36]y, 74% men, 69% Caucasian, 29% Indigenous). Compared with anti-HCV-negative PWID, anti-HCV-positive PWID reported injecting more morphine and hydromorphone, using more hydromorphone via non-injection routes, and were more likely to be enrolled in methadone programs. More than 60% reported previous HCV testing, but recent testing (<2 y) was more frequent in the anti-HCV-negative group ( p = 0.03). All were HBV negative, but more than 50% of the anti-HCV-positive group had anti-HBs titres more than 10 IU/L compared with 35% of the anti-HCV-negative group ( p = 0.01), and 3 of 257 were HIV positive (1 co-infected with HCV–HIV). CONCLUSIONS: In this prospective study, differences in age, timing of HCV testing and risk behaviours were found between anti-HCV-positive and anti-HCV-negative groups.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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".