Poor adherence to HIV monitoring and treatment guidelines for HIV‐infected injection drug users
Bibliographic record
Abstract
OBJECTIVES: There is growing concern about access to HIV/AIDS care among injection drug users (IDUs). We examined rates of CD4 cell count monitoring and correlates among HIV-infected IDUs. METHODS: This prospective observational cohort study of 460 community-recruited HIV-infected IDUs was situated in a Canadian city where all medical care is provided free of charge. Over a median follow-up period of 76 months, we evaluated factors associated with CD4 cell count monitoring through a linkage with a centralized CD4 registry. RESULTS: Overall, <5% of IDUs had CD4 monitoring consistent with local therapeutic guidelines. In multivariate analyses, after adjustment for being on antiretroviral therapy [odds ratio (OR) 2.21, 95% confidence interval (CI) 1.84-2.70, P<0.001] female gender (OR 0.71, 95% CI 0.57-0.89, P=0.003), non-White ethnicity (OR 0.75, 95% CI 0.60-0.94, P=0.014), use of methadone maintenance therapy (OR 1.66, 95% CI 1.42-1.94, P<0.001) and daily heroin use (OR 0.72, 95% CI 0.61-0.85, P<0.001) were independently associated with CD4 monitoring. CONCLUSIONS: Strategies to improve CD4 surveillance among IDUs are critically important, particularly for female and non-White IDUs. Expanded treatment for heroin dependence appears to have the greatest potential for improved care.
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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 | 0.002 | 0.010 |
| 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) | 0.002 | 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".