Limited Impact of First-Line Drug Resistance Mutations on Virologic Response Among Patients Receiving Second-Line Antiretroviral Therapy in Rural Uganda
Bibliographic record
Abstract
BACKGROUND: Delayed detection of ART failure in settings without access to viral load (VL) monitoring has been hypothesized to lead to suboptimal response to second-line therapy due to accumulated drug resistance mutations (DRMs). We tested this hypothesis in a program setting in rural Uganda. METHODS: From June 2012 to January 2014, we enrolled participants receiving nonnucleoside reverse transcriptase inhibitor-based first-line ART for ≥4 years, without access to VL monitoring. Participants who had a measured VL ≥ 1000 copies/mL on two occasions were switched to protease inhibitor-based regimens and followed every 6 months until September 2016. We measured VL at study exit. We conducted DRM testing at enrollment and study exit and examined factors associated with virologic failure. RESULTS: We enrolled 137 participants (64.3% female) with a median age of 44 years and a median duration on ART of 6.0 years. In a median of 2.8 years of follow-up, 7 (5%) died, 5 (3.6%) voluntarily withdrew, and 9 (6.6%) became lost to follow-up. Of 116 participants with a VL result at study exit, 20 (17%) had VL > 1000 copies/mL. Virologic failure was associated with reporting suboptimal adherence ( P = 0.028). Of patients with DRM data at enrollment, 103 of 105 (98%) had at least 1 DRM. Participants with thymidine analog mutations at enrollment were less likely to have virologic failure at study exit (11% vs. 36%; P = 0.007). No other DRMs were associated with failure. CONCLUSION: Even in the presence of multiple DRMs on first-line therapy, virologic failure after 3 years of protease inhibitor-based ART was infrequent. Suboptimal adherence to ART was associated with virologic failure.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".