Point-of-care tests for HIV drug resistance monitoring: an update of the literature and future viewpoints
Bibliographic record
Abstract
INTRODUCTION: HIV drug resistance (HIVDR) threatens global antiretroviral therapy (ART) success, especially as treatment scales up in resource-limited settings (RLS). Conventional genotypic HIVDR testing relies on complex instrumentation and often involves long turnaround time, creating critical gaps in managing virologic failure. Point-of-care test (POCT) technologies offer the potential of same-day resistance detection and immediate treatment optimization at the site of care. AREAS COVERED: This review examines potential HIVDR POCT technologies, with primary focus on advances from 2022 to 2025. It evaluates both established platforms and emerging approaches. Each technology is assessed against WHO REASSURED criteria for point-of-care diagnostics. It also summarizes the relevant clinical validation data, early field implementation experiences, and their feasibility of integration into existing laboratory systems in low- to middle-income countries (LMICs). EXPERT OPINION: While no single platform currently fulfills all REASSURED criteria, several show strong potentials for near-term implementation, particularly OLA-Simple. Multi-country validation studies support its utility; however, PCR dependence still limits POC usage. Key challenges remain, including limited HIVDR mutations coverage, reliance on complex lab instrumentation, and high costs that hinder scalability and long-term sustainability. By 2030, routinized HIVDR POCT could transform HIV care by enabling real-time treatment decisions, even in RLS or LMICs.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".