Bell’s Palsy and COVID-19 Vaccines: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
INTRODUCTION: Once the COVID-19 pandemic was declared, the world was waiting for the clue that would be able to cut down the progression of the disease. Vaccines play a crucial role in reducing the disease and saving many people worldwide. However, there are several side effects of these vaccines, like pain, fatigue, fever, and neurological defects like Bell's palsy. In this systematic review, we presented evidence about the occurrence of Bell's palsy followed by COVID-19 vaccination. METHODS: We searched PubMed, SCOPUS, EBSCO, and Web of Science (WOS) from inception till October 2022. The quality assessment was conducted using the Joanna Briggs Institute, National Institute of Health, and Newcastle-Ottawa. The analysis was conducted on SPSS. RESULTS: Thirty-five records were involved in our study. The results of our cases revealed that most of the patients (62.8%) experienced unilateral facial paralysis. Also, the majority of the cases were reported after the first dose, and most cases were after Pfizer, AstraZeneca, and Sputnik V vaccines, respectively. The patients who were treated with corticosteroids, IVIG, and anti-viral drugs, showed marked recovery afterward. CONCLUSION: The rate ratio of Bell's palsy after COVID-19 vaccination was 25.3 per 1,000,000. The ratio was higher after the first dose compared to the second dose and was higher among those who took Oxford/AstraZeneca vaccine compared to other vaccines. However, this condition was reported in a small number of cases among a large number of vaccinated people worldwide. It is important to note that the benefits of getting vaccinated far outweigh any potential risks.
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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 | 0.010 | 0.030 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.024 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".