The Safety Profile of COVID-19 Vaccines: A Narrative Review
Bibliographic record
Abstract
The ongoing COVID-19 pandemic inflicted a considerable burden on health systems and individuals world-wide. Thus, scientists intended to propose beneficial treatments and vaccines to fight against this virus.However, vaccination remained the only effective way to reduce death and hospitalization due to COVID-19infection. To date, about five proposed COVID-19 vaccines have been approved as WHO emergency uselistings (EUL). Yet, their safety profile needs the following actions to be revealed: (1) more follow-up registrysystems and (2) global clinical trials in various countries in a period that they are experiencing a peak.By searching keywords 'COVID-19' and 'vaccination' in PubMed and Scopus databases, we aimed to sum-marize the current evidence in the literature regarding the safety profile (i.e., local and systemic adverseevents) of ten COVID-19 vaccines: (1) Pfizer/BioNTech, (2) Moderna, (3) Sputnik V, (4) Bharat, (5) CanSi-no, (6) Sinovac, (7) AstraZeneca, (8) Johnson & Johnson, (9) Novavax, and (10) Sinopharm. Moreover, wedemonstrated the data on the safety of heterologous schedules of these vaccines alongside further consider-ations in people with comorbidities and particular circumstances.Most of the COVID-19 vaccine adverse effects possess a mild-to-moderate, self-limiting nature. However,special circumstances such as severe hyper-sensitivity necessitate the use of an alternate COVID-19 vaccine.Vaccination is the only way to exit the global pandemic, and its benefits outweigh its adverse effects. Mean-while, people should be aware of the signs of the probable rare, severe reactions to the vaccine.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| 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) | 0.005 | 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".