Patterns of heroin and cocaine injection and plasma HIV-1 RNA suppression among a long-term cohort of injection drug users
Bibliographic record
Abstract
Background Previous studies suggest that active drug use may compromise HIV treatment among HIV-positive injection drug users (IDU). However, little is known about the differential impacts of cocaine injection, heroin injection, and combined cocaine and heroin injection on plasma HIV-1 RNA suppression. Methods Data were derived from a longstanding open prospective cohort of HIV-positive IDU in Vancouver, Canada. Kaplan-Meier methods and Cox proportional hazards regression were used to examine the impacts of different drug use patterns on rates of plasma HIV-1 RNA suppression. Results Between May 1996 and April 2008, 267 antiretroviral (ART) naïve participants were seen for a median follow-up duration of 50.6 months after initiating ART. The incidence density of HIV-1 RNA suppression was 65.2 (95%CI: 57.0–74.2) per 100 person-years. In Kaplan-Meier analyses, compared to those who abstained from injecting, individuals injecting heroin, cocaine, or combined heroin/cocaine at baseline were significantly less likely to achieve viral suppression (all p < 0.01). However, none of the drug use categories remained associated with a reduced rate of viral suppression when considered as time-updated variables (all p > 0.05). Conclusions Active injecting at the time of ART initiation was associated with lower plasma HIV-1 RNA suppression rates; however, there was no difference in suppression rates when drug use patterns were examined over time. These findings imply that adherence interventions for active injectors should optimally be applied at the time of ART initiation.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".