Alcohol Consumption and Antiretroviral Adherence Among HIV‐Infected Persons With Alcohol Problems
Bibliographic record
Abstract
BACKGROUND: Alcohol abuse has been associated with poor adherence to highly active antiretroviral therapy (HAART). We examined the relative importance of varying levels of alcohol consumption on adherence in HIV-infected patients with a history of alcohol problems. METHODS: We surveyed 349 HIV-infected persons with a history of alcohol problems at 6-month intervals. Of these subjects, 267 were taking HAART at one or more time periods during the 30-month follow-up period. Interviews assessed recent adherence to HAART and past month alcohol consumption, defined as "none", "moderate", and "at risk". We investigated the relationship between adherence to HAART and alcohol consumption at baseline and at each subsequent 6-month follow-up interval using multivariable longitudinal regression models, while controlling for potential confounders. RESULTS: Among the 267 HIV-infected persons with a history of alcohol problems who were receiving HAART, alcohol consumption was the most significant predictor of adherence (p < 0.0001), with better adherence being associated with recent abstinence from alcohol, compared with at-risk level usage (odds ratio = 3.6, 95% confidence interval = 2.1-6.2) or compared with moderate usage (odds ratio = 3.0, 95% confidence interval = 2.0-4.5). CONCLUSIONS: Any alcohol use among HIV-infected persons with a history of alcohol problems is associated with worse HAART adherence. Addressing alcohol use in HIV-infected persons may improve antiretroviral adherence and ultimately clinical 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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".