Need assessment for HIV drug resistance testing and landscape of current and future technologies in low- and middle-income countries
Bibliographic record
Abstract
Resistance to antiretroviral drugs used to treat HIV is an important and evolving concern, particularly in low- and middle-income countries (LMICs) which have been impacted to the greatest extent by the HIV pandemic. Efforts to monitor the emergence and transmission of resistance over the past decade have shown that drug resistance-especially to the nucleoside analogue and non-nucleoside reverse transcriptase inhibitors-can (and have) increased to levels that can jeopardize the efficacy of available treatment options at the population level. The global shift to integrase-based regimens as the preferred first-line therapy as well as technological advancements in the methods for detecting resistance have had an impact in broadening and diversifying the landscape of and use case for HIV drug resistance testing. This review estimates the potential demand for HIV drug resistance tests, and surveys current testing methodologies, with a focus on their application in LMICs.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 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".