A randomized controlled trial of HIV therapeutic vaccination using ALVAC with or without Remune
Bibliographic record
Abstract
OBJECTIVES: Therapeutic HIV vaccination during the time of virologic suppression may delay or blunt viral load rebound after interruption of antiretroviral therapy (ART). The use of ALVAC, to enhance cytotoxic T-lymphocyte responses, with Remune, which provides CD4 T-cell help, may induce anti-HIV responses capable of controlling viral replication. METHODS: CTN173 was a randomized, placebo-controlled double-blind study in which effectively treated HIV-infected individuals (viral load <50 copies/ml for more than 2 years) with CD4 nadir more than 250 cells/μl and current CD4 cell counts more than 500 cells/μl were randomized to receive: ALVAC with Remune, ALVAC alone or matching placebos over 20 weeks. At week 24, participants interrupted ART with intensive clinical, virologic and immunologic monitoring to week 48. RESULTS: Baseline characteristics of the 52 randomized participants were balanced between arms. Forty-eight participants who received all vaccinations interrupted ART at week 24. Median time to viral load more than 50 copies/ml tended to be greater in the two vaccine arms (24.5, 23.0 vs. 13.5 days in the placebo arm, P = 0.097 for combined vaccine groups vs. placebo), but subsequent viral load set-point was not different between groups. Significantly fewer participants in the two vaccine arms restarted ART or met CD4 criteria to do so (P = 0.024). CONCLUSION: Although ALVAC with or without Remune did not lower the viral load set-point, it tended to delay viral load rebound and was associated with a greater time to meet preset criteria to restart ART. Further investigations of those individuals who derived benefit from vaccination could provide important insights into HIV therapeutic vaccine development.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".