COVID-19 Vaccination Induced Neurological Complications; A Systematic Literature Review
Bibliographic record
Abstract
Introduction: Vaccination against COVID-19 is proved successful in reducing the transmission of disease globally along with reducing the severity of disease but vaccine associated adverse effects (VAERS) questions the safety profile of COVID-19 vaccines especially the serious neurological adverse effects that are responsible for post-vaccination mortalities globally. Aim: To review the neurological adverse effects of COVID-19 vaccines and identify their possible pathophysiology Method: The literature search was conducted by two researchers. The database of Google Scholar was used to search relevant literature. All the articles published between 1st Jan, 2018 to 1st Jan 2022 were screened for inclusion and exclusion criteria. Results: The search strategy resulted in 278 articles of which, 1duplicates were removed and 277 articles were screened according to inclusion exclusion criteria, 103 articles were excluded and a total of 20 articles were included in review of which there were 17 case reports,1 observational studies, 1 case series and 1 case-control study. Conclusion: The findings of review provide a deep insight of neurological complications that can occur in individuals after receiving COVID-19 vaccination and the clinicians should be conscious while dealing with a patient with new-onset neurological symptoms after vaccination against COVID-19 unless a causal association is developed between the vaccine and serious neurological adverse events by future researches. Keywords: COVID-19, Post-vaccination, Neurological Complications, Vaccine Associated Adverse events, Acute Ischemic Stroke and GBS
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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 | 0.005 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.011 | 0.012 |
| Science and technology studies | 0.001 | 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".