Safety and Adverse Effects Related to COVID-19 Viral Vector Vaccines: A Systematic Review.
Bibliographic record
Abstract
Background: There have been safety concerns regarding the COVID-19 vaccines because of their unprecedented speed of development. Therefore, systematic reviews are necessary to address these concerns and reduce public hesitancy regarding COVID-19 vaccines. This study aims to systematically review the reported adverse events related to viral vector COVID-19 vaccines. Materials and Methods: , 2021. This study adheres to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) checklist. The records underwent two-step title/abstract and full-text screenings, and the eligible records were included in the data extraction process. We used the Newcastle-Ottawa Scale (NOS) for the Bias Assessment of included articles. Results: The adenovirus vector-based COVID-19 vaccines, including the Janssen COVID-19 vaccine, the AstraZeneca COVID-19 vaccine, and the Sputnik V vaccine were included in this review. Among these vaccines, the AstraZeneca has presented enormous side effects with most being systemic and a few sporadic cases of life-threatening events such as thrombosis and capillary leak syndrome and even death in a few cases. Prominent systemic side effects of the adenovirus vaccines include fever, fatigue, malaise, arthralgia, myalgia, sweating, and dizziness. Erythema, swelling, tenderness, itching, and numbness at the injection site are the most common local reactions. Conclusion: It appeared that the frequency of serious adverse events is negligible, and vaccination to prevent severe COVID-19 and mortality has greater benefits than adverse events in the general population.
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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 | 0.011 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.009 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".