Markers of poor adherence among adults with HIV attending Themba Lethu HIV Clinic, Helen Joseph Hospital, Johannesburg, South Africa
Bibliographic record
Abstract
Background: To date, there is no consensus on ideal ways to measure antiretroviral treatment (ART) adherence in resource limited settings. This study aimed to identify markers of poor adherence to ART. Methods: Retrospective data of HIV-positive ART-naïve adults initiating standard first-line ART at Themba Lethu Clinic, Helen Joseph Hospital, Johannesburg, South Africa from April 2004 to December 2011 were analysed. Poisson regression models with robust error variance were used to assessed the following potential markers of poor adherence 'last self-reported adherence, missed clinic visits, mean corpuscular volume (MCV), CD4 count against definition of adherence, suppressed HIV viral load using traditional test metrics'. Results: A total of 11 724 patients were eligible; 1712 (14.6%) had unsuppressed viral load within 6 months after initiating ART. The main marker of poor adherence was a combination of change in CD4 count and MCV; change in CD4 ≥expected and change in MCV <14.5 fL (RR 2.82, 95% CI 2.16-3.67), change in CD4 <expected and change in MCV <14.5 fL (RR 5.49, 95% CI 4.13-7.30) compared to change in CD4 ≥expected and change in MCV ≥14.5 fL. Conclusions: A combination of less than expected increase in CD4 and MCV at 6 months after treatment initation was found to be a marker of poor adherence. This could help identify and monitor poor treatment adherence in the absence of viral load testing.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".