HIV-infected individuals who use alcohol and other drugs, and virologic suppression
Bibliographic record
Abstract
People living with HIV (PLWH) on antiretroviral therapy (ART) who use substances were examined to (a) describe those with virologic control and (b) determine which substance use-factors are associated with lack of virologic control. Participants were adult PLWH taking ART with either past 12-month DSM-IV substance dependence or past 30-day alcohol or illicit drug use. Substance use factors included number of DSM-IV alcohol or drug dependence criteria and past 30-day specific substance use. Associations with HIV viral load (HVL) (<200 vs. ≥200 copies/mL) were tested using logistic regression models. Multivariable analyses adjusted for age, sex, homelessness and anxiety or depression. Participants (n = 202) were median age 50 years, 66% male, 51% African American and 75% self-reported ≥90% past 30-day ART adherence. Though HVL suppression (HVL <200 copies/mL) was achieved in 78% (158/202), past 30-day substance use was common among this group: 77% cigarette use; 51% heavy alcohol use; 50% marijuana; 27% cocaine; 16% heroin; and 15% illicit prescription opioid use. After adjusting for covariates, specific substance use was not associated with a detectable HVL, however number of past 12-month DSM-IV drug dependence criteria was (adjusted odds ratio = 1.23 for each additional criterion, 95% CI: 1.04-1.46). Three-quarters of a substance-using cohort of PLWH receiving ART had virologic control and ≥90% ART adherence. Substance dependence criteria (particularly drug dependence), not specifically substance use, were associated with lack of virologic control. Optimal HIV outcomes can be achieved by individuals who use alcohol or drugs and addressing symptoms of substance dependence may improve HIV-related outcomes.
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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 | 0.002 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".