Identifying Risk of Viral Failure in Treated HIV-Infected Patients Using Different Measures of Adherence: The Antiretroviral Therapy Cohort Collaboration
Bibliographic record
Abstract
Adherence to antiretroviral therapy (ART) is critical for successful treatment of Human Immunodeficiency Virus (HIV), but comparisons across settings are difficult because adherence is measured in different ways. We examined utility of different adherence measures for identification of patients at risk of viral failure (VF). Eight cohorts in the ART Cohort Collaboration contributed data from pharmacy refills or self-report questionnaires collected between 1996 and 2013 (N = 11689). For pharmacy data (N = 7156), we examined associations of percentage adherence during the 1st year of ART with VF (>500 copies/mL) at 1 year. For self-report data (N = 4533), we examined 28-day adherence with VF based on closest viral load measure within 6 months after questionnaire date. Since adherence differed markedly by measurement type, we defined different cut-off points for pharmacy (lower <45%, medium 45–99%, higher 100%) and self-report (lower ≤95%, medium 96–99%, higher 100%) data. Adjusted odds ratios (ORs) for VF in lower and medium, compared to higher adherence groups, were 23.04 (95% CI: 18.44–28.78) and 3.84 (3.36–4.39) for pharmacy data. For self-report data, they were 3.19 (2.31–4.40) and 1.08 (0.80–1.46). Both types of measure were strongly associated with VF. Although adherence measurements over longer time-frames are preferable for prediction, they are less useful for intervention.
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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 | 0.022 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".