Differential predictors of ART adherence among HIV-monoinfected versus HIV/HCV-coinfected individuals
Bibliographic record
Abstract
Although adherence is an important key to the efficacy of antiretroviral therapy (ART), many people living with HIV (PLWH) fail to maintain optimal levels of ART adherence over time. PLWH with the added burden of Hepatitis C virus (HCV) coinfection possess unique challenges that potentially impact their motivation and ability to adhere to ART. The present investigation sought to (1) compare ART adherence levels among a sample of HIV/HCV-coinfected versus HIV-monoinfected patients, and (2) identify whether ART-related clinical and psychosocial correlates differ by HCV status. PLWH receiving ART (N = 215: 105 HIV/HCV-coinfected, 110 HIV-monoinfected) completed a comprehensive survey assessing ART adherence and its potential correlates. Medical chart extraction identified clinical factors, including liver enzymes. Results demonstrated that ART adherence did not differ by HCV status, with 83.7% of coinfected patients and 82.4% of monoinfected patients reporting optimal (i.e., ≥95%) adherence during a four-day recall period (p = .809). Multivariable logistic regression demonstrated that regardless of HCV status, optimal ART adherence was associated with experiencing fewer adherence-related behavioral skills barriers (AOR = 0.56; 95%CI = 0.43-0.73), lower likelihood of problematic drinking (AOR = 0.15; 95%CI = 0.04-0.67), and lower likelihood of methamphetamine use (AOR = 0.14; 95%CI = 0.03-0.69). However, among HIV/HCV-coinfected patients, optimal adherence was additionally associated with experiencing fewer ART adherence-related motivational barriers (AOR = 0.23; 95%CI = 0.08-0.62) and lower likelihood of depression (AOR = 0.06; 95%CI = 0.00-0.84). Findings suggest that although HIV/HCV-coinfected patients may face additional, distinct barriers to ART adherence, levels of adherence commensurate with those demonstrated by HIV-monoinfected patients might be achievable if these barriers are addressed.
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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 | 0.003 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".