Substance Abuse Treatment and Hospitalization among a Cohort of HIV‐Infected Individuals with Alcohol Problems
Bibliographic record
Abstract
BACKGROUND: We examined the association of substance abuse treatment services on hospitalization among participants in the HIV-Alcohol Longitudinal Cohort (HIV-ALC) study of HIV-infected individuals with a history of alcohol problems. METHODS: A standardized questionnaire that inquired about demographics, substance use, use of substance abuse treatment services, and hospitalization was administered to 349 HIV-ALC participants. We defined substance abuse treatment services as any of the following in the past 6 months: 12 weeks in a half-way house or residential facility, 12 visits to a substance abuse counselor or mental health professional, or participation in any methadone maintenance program. RESULTS: Almost one third of this cohort were hospitalized in the past 6 months. Substance abuse treatment was not significantly associated with hospitalization [adjusted odds ratio (AOR) 1.0; 95% confidence interval (CI) 0.7-1.5), whereas homelessness (AOR 2.3; 95% CI 1.5-3.6), injection drug use (AOR 1.7; 95% CI 1.0-2.7), severity of alcohol dependence (AOR 1.02; 95% CI 1.00-1.05), CD4 cell count (AOR 0.999; 95% CI 0.998-1.00), and HIV RNA (AOR 1.1; 95% CI 1.0-1.2) were independently associated with increased odds of hospitalization over time. CONCLUSIONS: Engagement in substance abuse treatment was not associated with a decrease in hospital use by HIV-infected individuals with a history of alcohol problems. The period of substance abuse treatment may present an opportunity to address health care utilization patterns of HIV-infected individuals.
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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 | 0.001 |
| 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 | 0.000 | 0.001 |
| 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".