Casting a Wide Net: HIV Drug Resistance Monitoring in Pre-Exposure Prophylaxis Seroconverters in the Global Evaluation of Microbicide Sensitivity Project
Bibliographic record
Abstract
BACKGROUND: Evidence of HIV drug resistance (HIVDR) in individuals using oral pre-exposure prophylaxis (PrEP) who acquire HIV is limited to clinical trials and case studies. More data are needed to understand the risk of HIVDR with oral PrEP during PrEP rollout. Mechanisms to collect these data vary, and are dependent on cost, scale of PrEP distribution, and in-country infrastructure for the identification, collection, and testing of samples from PrEP seroconverters. METHODS: The Global Evaluation of Microbicide Sensitivity (GEMS) project, in collaboration with country stakeholders, initiated HIVDR monitoring among new HIV seroconverters with prior PrEP use in Eswatini, Kenya, South Africa, and Zimbabwe. Standalone protocols were developed to assess HIVDR among a national sample of PrEP users. In addition, HIVDR testing was incorporated into existing demonstration projects for key populations. LESSONS LEARNED: Countries are supportive of conducting a time-limited evaluation of HIVDR during the early stages of PrEP rollout. As PrEP rollout expands, the need for long-term HIVDR monitoring with PrEP will need to be balanced with maintaining national HIV drug resistance surveillance for pretreatment and acquired drug resistance. Laboratory capacity is a common obstacle to setting up a monitoring system. CONCLUSIONS: Establishing HIV resistance monitoring within PrEP programs is feasible. Approaches to drug resistance monitoring may evolve as the PrEP programs mature and expand. The methods and implementation support offered by GEMS assisted countries in developing methods to monitor for drug resistance that best fit their PrEP program needs and resources.
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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 | 0.011 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| 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".