Behavioural trajectories following DAA treatment for HCV among people with HIV: findings from an international consortium of prospective cohort studies
Bibliographic record
Abstract
OBJECTIVE: Examine the proportion of people with HIV engaging in behaviours associated with hepatitis C virus (HCV) infection after successful direct-acting antiviral (DAA) treatment and establish longitudinal patterns of behavioural risk over time. DESIGN: Multinational, prospective cohort study (International Collaboration on Hepatitis C Elimination in HIV Cohorts). METHODS: Individuals with HIV successfully treated with DAAs and ≥2 follow-up visits with behavioural data were included. Changes in the proportion of any risk behaviour after treatment, which included sexual and drug use behaviours, were analysed using logistic regression with generalized estimating equations. We identified distinct trajectories of any risk behaviour over time using group-based trajectory models (GBTM). RESULTS: Of the 1,477 individuals included, 487 (33.0%) were people who inject drugs, 378 (25.6%) were men who have sex with men and 442 (29.9%) were both. During a median 2.7 years (IQR = 1.6-3.9) of follow-up, the proportion engaging in any risk behaviour slightly decreased over time (adjusted odds ratio per half year = 0.97, 95% confidence interval = 0.95-0.99). GBTM revealed four distinct behavioural trajectories: consistently low ( n = 433, 29.3% of total population), moderate at baseline and increasing ( n = 119, 8.1%), high at baseline and decreasing ( n = 184, 12.5%) and consistently high ( n = 741, 50.2%). CONCLUSIONS: Despite slight decreases in behaviours following successful DAA treatment, half of individuals had a consistently high probability of behaviours that put them at risk of HCV reinfection over time. As reinfections comprise a growing proportion of new incident HCV cases, these findings underscore the importance of ongoing primary prevention measures alongside testing and retreatment to eliminate HCV.
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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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".