Preparing developing countries for efficacy trials
Bibliographic record
Abstract
PURPOSE OF REVIEW: For the first time, Africa is poised to test the efficacy of two candidate vaccines. This raises many scientific, logistic, regulatory and ethical challenges for the continent. This review outlines recent developments relating to the epidemiologic, scientific, site development, and standard of care issues relevant to the conduct of these trials in developing countries. RECENT FINDINGS: The AIDS epidemic in Africa has reached crisis proportions. Despite more than 20 years having passed since the discovery of HIV, there are no effective biomedical interventions. The testing of two adenovirus type 5-vectored HIV vaccine candidates for efficacy is crucial. These vaccines, which seek to elicit cytotoxic T lymphocyte responses, may not prevent infection, but may ameliorate infection and potentially prevent secondary HIV transmission. Efficacy of these vaccines may be impacted by the presence of pre-existing immunity to the vectors and the genetic diversity of HIV. Trials will be conducted in areas of the world with high HIV incidence, and special efforts should be made to enroll young women and adolescents. The development of clinical trial site capacity, technology transfer of immunogenicity assays to in-country laboratories, and expediting high-quality regulatory and ethical review and executing efficacy trials of the highest standard should be seen as paramount by donors, vaccine developers, clinical trial networks and developing world governments. SUMMARY: HIV vaccine efficacy trials will soon be conducted in Africa.
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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 | 0.175 | 0.247 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.009 | 0.006 |
| Insufficient payload (model declined to judge) | 0.020 | 0.011 |
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".