Reactogenicity and immunogenicity of heterologous prime-boost immunization with COVID-19 vaccine
Bibliographic record
Abstract
BACKGROUND: The objective of the present work was to assess the reactogenicity and immunogenicity of heterologous COVID-19 vaccination regimens in clinical trials and observational studies. METHODS: PubMed, Cochrane Library, Embase, MedRxiv, BioRxiv databases were searched in September 29, 2021. The PRISMA instruction for systemic review was followed. Two reviewers independently selected the studies, extracted the data and assessed risk of bias. The quality of studies was evaluated using the New Castle-Ottawa and Cochrane risk of instrument. The characteristics and study outcome (e.g., adverse events, immune response, and variant of concern) were extracted. RESULTS: Nineteen studies were included in the final data synthesis with 5 clinical trials and 14 observational studies. Heterologous vaccine administration showed a trend toward more frequent systemic reactions. However, the total reactogenicity was tolerable and manageable. Importantly, the heterologous prime-boost vaccination regimens provided higher immunogenic effect either vector/ mRNA-based vaccine or vector/ inactivated vaccine in both humoral and cellular immune response. Notably, the heterologous regimens induced the potential protection against the variant of concern, even to the Delta variant. CONCLUSIONS: The current findings provided evidence about the higher induction of robust immunogenicity and tolerated reactogenicity of heterologous vaccination regimens (vector-based/mRNA vaccine or vector-based/inactivated vaccine). Also, this study supports the application of heterologous regimens against COVID-19 which may provide more opportunities to speed up the global vaccination campaign and maximize the capacity to control the pandemic.
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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 | 0.038 | 0.083 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".