Antiretroviral Therapy in Prevention of HIV and TB: Update on Current Research Efforts
Bibliographic record
Abstract
There is considerable scientific evidence supporting the use of antiretroviral therapy (ART) in prevention of human immunodeficiency virus (HIV) and tuberculosis (TB) infections. The complex nature of the HIV and TB prevention responses, resource constraints, remaining questions about cost and feasibility, and the need to use a solid evidence base to make policy decisions, and the implementation challenges to translating trial data to operational settings require a well-organised and coordinated response to research in this area. To this end, we aimed to catalogue the ongoing and planned research activities that evaluate the impact of ART plus other interventions on HIV- and/or TB-related morbidity, mortality, risk behaviour, HIV incidence and transmission. Using a limited search methodology, 50 projects were identified examining ART as prevention, representing 5 regions and 52 countries with a global distribution. There are 24 randomised controlled clinical trials with at least 12 large randomised individual or community cluster trials in resource-constrained settings that are in the planning or early implementation stages. There is considerable heterogeneity between studies in terms of methodology, interventions and geographical location. While the identified studies will undoubtedly advance our understanding of the efficacy and effectiveness of ART for prevention, some key questions may remain unanswered or only partially answered. The large number and wide variety of research projects emphasise the importance of this research issue and clearly demonstrate the potential for synergies, partnerships and coordination across funding agencies.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".