The Role of At-Risk Alcohol/Drug Use and Treatment in Appointment Attendance and Virologic Suppression Among HIV <sup>+</sup> African Americans
Bibliographic record
Abstract
The causes of poor clinic attendance and incomplete virologic suppression among HIV(+) African Americans (AAs) are not well understood. We estimated the effect of at-risk alcohol/drug use and associated treatment on attending scheduled appointments and virologic suppression among 576 HIV(+) AA patients in the University of Alabama at Birmingham (UAB) 1917 Clinic Cohort who contributed 591 interviews to the analysis. At interview, 78% of patients were new to HIV care at UAB, 38% engaged in at-risk alcohol/drug use or received associated treatment in the prior year, while the median (quartiles) age and CD4 count were 36 (28; 46) years and 321 (142; 530) cells/μl, respectively. In the 2 years after an interview, half of the patients had attended at least 82% of appointments while half had achieved virologic suppression for at least 71% of RNA assessments. Compared to patients who did not use or receive treatment, the adjusted risk ratio (aRR) for attending appointments for patients who did use but did not receive treatment was 0.97 (95% confidence limits: 0.92, 1.03). The corresponding aRR for virologic suppression was 0.94 (0.86, 1.03). Compared to patients who did not receive treatment but did use, the aRR for attending appointments for patients who did receive treatment and did use was 0.86 (0.78, 0.95). The corresponding aRR for virologic suppression was 1.07 (0.92, 1.24). Use was negatively associated with attendance and virologic suppression among patients not in treatment. Among users, treatment was negatively associated with attendance yet positively associated with virologic suppression. However, aRR estimates were imprecise.
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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 | 0.004 | 0.010 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".