HIV-1 Immunogen: an overview of almost 30 years of clinical testing of a candidate therapeutic vaccine
Bibliographic record
Abstract
INTRODUCTION: Although current antiretroviral therapy (ART) has transformed HIV infection into a chronic, manageable disease, ART does not cure HIV infection. Furthermore, the majority of the world's infected individuals live in resource-limited countries in which access to ART is limited. Thus, the development of an effective therapeutic HIV vaccine would be an invaluable treatment alternative. AREAS COVERED: Developed by the late Dr. Jonas Salk, HIV-1 Immunogen (Remune®) is a candidate therapeutic vaccine that has been studied in thousands of HIV-infected individuals in more than a dozen clinical trials during almost three decades. This Drug Evaluation, which summarizes the results of these trials that have shown the vaccine to be safe and immunogenic, also discusses the contradictory and controversial conclusions drawn from the phases 2, 2/3 and 3 trials that assessed the clinical efficacy of this vaccine. EXPERT OPINION: Given the lack of unequivocal clinical benefits of HIV-1 Immunogen despite almost 30 years of extensive testing, it does not appear, in our view, that this vaccine is a clinically effective immunotherapy. However, inclusion of this vaccine in the newly proposed 'Kick/Shock and Kill' strategy for HIV eradication, or use as a prophylactic vaccine, could be considered for future trials.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".