The Future Face of Coinfection
Bibliographic record
Abstract
The purpose of this study was to determine the prevalence and incidence of HIV and hepatitis C virus (HCV) coinfection among young (aged 29 years or younger) injection drug users (IDUs) and to compare sociodemographic and risk characteristics between (HIV/HCV) coinfected, monoinfected, or HIV- and HCV-negative youth. Data were collected through the Vancouver Injection Drug Users Study (VIDUS). To date, more than 1400 IDUs have been enrolled and followed, of whom 479 were aged 29 years or younger. Semiannually, participants have completed an interviewer-administered questionnaire and have undergone serologic testing for HIV and HCV. Univariate and multivariate logistic regression analyses were undertaken to investigate predictors of baseline coinfection. Cox regression models with time-dependent covariates were used to identify predictors of time to secondary infection seroconversion. A Cochran-Armitage trend test was used to determine risk associations across 3 categories: no infection, monoinfection, and coinfection. Of the 479 young injectors, 78 (16%) were coinfected with HIV and HCV at baseline and a further 45 (15%) with follow-up data became coinfected during the study period. Baseline coinfection was independently associated with being female, being aboriginal, older age, greater number of years injecting, and living in the IDU epicenter. Factors independently associated with time to secondary infection seroconversion were borrowing needles and greater than once-daily cocaine injection, and accessing methadone maintenance therapy in the previous 6 months was protective. There were clear trends across the 3 categories for increasing proportions of female subjects, aboriginal subjects, older age, greater number of years injecting, living in the IDU epicenter, and daily cocaine use. There were a shocking number of youth living with coinfection, particularly female and aboriginal youth. The median number of years injecting for youth seroconverting to a secondary infection was 3 years, suggesting that appropriate public health interventions should be implemented immediately.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.018 | 0.003 |
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".