Ad26.COV2.S viral vector vaccine’s safety and immunogenicity: A review of literature
Bibliographic record
Abstract
COVID-19 is a respiratory infectious disease that spreads through droplets. This disease has brought immense changes that impacted all countries around the world and the healthcare system in many ways. Developing an effective vaccine has been a high priority and clinical trials are continuously conducted to improve their efficacy. There are two current competing forms of the vaccine: the first is mRNA vaccines, where they act as a carrier for immunological information encoding for the antigen (spike proteins) and induce an immune response without interacting with the genome. Although they have an effectiveness of 95%, they do require two doses to be fully effective. On the other hand, viral vector-based vaccines use a vector to deliver the genetic code for the antigen and, like a normal infection, uses the body cell’s machinery to produce more antigen, triggering an immune response. Many clinical trials are being done to improve and evaluate its efficacy as this vaccine provides substantial potential advantages, one of which includes requiring only one dosage to be vaccinated to reach greater effectiveness in inducing antibody and CD4 T cells production. In consideration of the WHO SAGE Roadmap for vaccine prioritization, the aim of this study is to provide a review of the current literature and clinical trials being conducted on the viral vector vaccine Ad26.COV2.S’s safety and immunogenicity. In addition, it assesses potential future directions, implications and the substantial benefits towards the health care system and at-high-risk populations.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".