Methadone maintenance therapy and viral suppression among HIV-infected opioid users: the impacts of crack and injection cocaine use
Bibliographic record
Abstract
Background—Methadone maintenance therapy (MMT) is associated with improved HIV treatment outcomes among people who use drugs (PWUD). The extent to which these benefits are sustained in the context of ongoing cocaine use is unclear. We assessed differential impacts of MMT on HIV viral load (VL) suppression in relation to discrete patterns of cocaine use. Methods—Data was drawn from ACCESS, a prospective cohort of HIV-positive PWUD in Vancouver, Canada. Generalized linear mixed-effects were used to model the independent effect of MMT on VL suppression across strata of frequency of cocaine injection and crack smoking (≥daily versus <daily), after adjustment for confounders. Results—The analysis included 397 HIV-positive opioid users who completed ≥1 study interview between 2005 and 2014. At baseline, 304 (77%) reported participation in MMT, 37 (9%) ≥ daily cocaine injection, and 158 (40%) ≥ daily crack smoking. In adjusted analyses, MMT remained independently associated with increased odds of VL suppression in both strata of crack smokers (AOR=3.11, 95% CI: 1.86–5.21 and AOR=1.48, 95%CI: 1.04–2.09, for ≥daily and <daily smokers, respectively), and among
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".