Absence of HIV-1 Drug Resistance Mutations Supports the Use of Dolutegravir in Uganda
Bibliographic record
Abstract
To screen for drug resistance and possible treatment with Dolutegravir (DTG) in treatment-naive patients and those experiencing virologic failure during first-, second-, and third-line combined antiretroviral therapy (cART) in Uganda. Samples from 417 patients in Uganda were analyzed for predicted drug resistance upon failing a first- (N = 158), second- (N = 121), or third-line [all 51 involving Raltegravir (RAL)] treatment regimen. HIV-1 pol gene was amplified and sequenced from plasma samples. Drug susceptibility was interpreted using the Stanford HIV database algorithm and SCUEAL was used for HIV-1 subtyping. Frequency of resistance to nucleoside reverse transcriptase inhibitors (NRTIs) (95%) and non-NRTI (NNRTI, 96%) was high in first-line treatment failures. Despite lack of NNRTI-based treatment for years, NNRTI resistance remained stable in 55% of patients failing second-line or third-line treatment, and was also at 10% in treatment-naive Ugandans. DTG resistance (n = 366) was not observed in treatment-naive individuals or individuals failing first- and second-line cART, and only found in two patients failing third-line cART, while 47% of the latter had RAL- and Elvitegravir-resistant HIV-1. Secondary mutations associated with DTG resistance were found in 2%-10% of patients failing third-line cART. Of 14 drugs currently available for cART in Uganda, resistance was readily observed to all antiretroviral drugs (except for DTG) in Ugandan patients failing first-, second-, or even third-line treatment regimens. The high NNRTI resistance in first-line treatment in Uganda even among treatment-naive patients calls for the use of DTG to reach the UNAIDS 90:90:90 goals.
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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 | 0.005 |
| 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".