High Levels of Dual-Class Drug Resistance in HIV-Infected Children Failing First-Line Antiretroviral Therapy in Southern Ethiopia
Bibliographic record
Abstract
Clinical monitoring of pediatric HIV treatment remains a major challenge in settings where drug resistance genotyping is not routinely available. As a result, our understanding of drug resistance, and its impact on subsequent therapeutic regimens available in these settings, remains limited. We investigate the prevalence and correlates of HIV-1 drug resistance among 94 participants of the Ethiopia Pediatric HIV Cohort failing first-line combination antiretroviral therapy (cART) using dried blood spot-based genotyping. Overall, 81% (73/90) of successfully genotyped participants harbored resistance mutations, including 69% (62/90) who harbored resistance to both Nucleoside Reverse Transcriptase Inhibitors (NRTIs) and Non-nucleoside Reverse Transcriptase Inhibitors (NNRTIs). Strikingly, 42% of resistant participants harbored resistance to all four NRTIs recommended for second-line use in this setting, meaning that there are effectively no remaining cART options for these children. Longer cART duration and prior regimen changes were significantly associated with detection of drug resistance mutations. Replicate genotyping increased the breadth of drug resistance detected in 34% of cases, and thus is recommended for consideration when typing from blood spots. Implementation of timely drug resistance testing and access to newer antiretrovirals and drug classes are urgently needed to guide clinical decision-making and improve outcomes for HIV-infected children on first-line cART in Ethiopia.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".