Drug use and phylogenetic clustering of hepatitis C virus infection among people who use drugs in Vancouver, Canada: A latent class analysis approach
Bibliographic record
Abstract
This study estimated latent classes (ie, unobserved subgroups in a population) of people who use drugs in Vancouver, Canada, and examined how these classes relate to phylogenetic clustering of hepatitis C virus (HCV) infection. HCV antibody-positive people who use drugs from two cohorts in Vancouver, Canada (1996-2012), with a Core-E2 sequence were included. Time-stamped phylogenetic trees were inferred, and phylogenetic clustering was determined by time to most common recent ancestor. Latent classes were estimated, and the association with the phylogenetic clustering outcome was assessed using an inclusive classify/analyse approach. Among 699 HCV RNA-positive participants (26% female, 24% HIV+), recent drug use included injecting cocaine (80%), injecting heroin (70%), injecting cocaine/heroin (ie, speedball, 38%) and crack cocaine smoking (28%). Latent class analysis identified four distinct subgroups of drug use typologies: (i) cocaine injecting, (ii) opioid and cocaine injecting, (iii) crack cocaine smoking and (iv) heroin injecting and currently receiving opioid substitution therapy. After adjusting for age and HIV infection, compared to the group defined by heroin injecting and currently receiving opioid substitution therapy, the odds of phylogenetic cluster membership was greater in the cocaine injecting group (adjusted OR [aOR]: 3.06; 95% CI: 1.73, 5.42) and lower in the crack cocaine smoking group (aOR: 0.06; 95% CI: 0.01, 0.48). Combining latent class and phylogenetic clustering analyses provides novel insights into the complex dynamics of HCV transmission. Incorporating differing risk profiles associated with drug use may provide opportunities to further optimize and target HCV treatment and prevention strategies.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".