Durability of non-nucleotide reverse transcriptase inhibitor-based first-line ART regimens after 7 years of treatment in rural Uganda
Bibliographic record
Abstract
ABSTRACT: Most antiretroviral therapy (ART) programs in resource-limited settings have historically used non-nucleotide reverse transcriptase inhibitor (NNRTI)-based regimens with limited access to routine viral load (VL) testing. We examined the long-term success of these regimens in rural Uganda among participants with 1 measured suppressed VL.We conducted a prospective cohort study of participants who had been on NNRTI-based first-line regimens for ≥4 years and had a VL <1000 copies/mL at enrollment in Jinja, Uganda. We collected clinical and behavioral data every 6 months and measured VL again after 3 years. We quantified factors associated with virologic failure (VF) (VL ≥ 1000 copies/mL) using Wilcoxon Rank Sum, chi-square, and Fisher's Exact Tests.We enrolled 503 participants; 75.9% were female, the median age was 45 years, and the median duration of time on ART was 6.8 years (IQR = 6.0-7.6 years). Sixty-nine percent of participants were receiving nevirapine, lamivudine, and zidovudine regimens; 22.5% were receiving efavirenz, lamivudine, and zidovudine; and 8.6% were receiving other regimens. Of the 479 with complete follow-up data, 12 (2.5%) had VL ≥ 1000 copies/mL. VF was inversely associated with reporting never missing pills (41.7% of VFs vs 72.8% non-VFs, P = .034). There were differences in distribution of the previous ART regimens (P = .005), but no clear associations with specific regimens. There was no association between having a VL of 50 to 999 copies/mL at enrollment and later VF (P = .160).Incidence of VF among individuals receiving ART for nearly 7 years was very low in the subsequent 3 years. NNRTI-based regimens appear to be very durable among those with good initial adherence.
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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 | 0.007 |
| 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 | 0.001 |
| 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".