Association of new onset seizure and <scp>COVID</scp>‐19 vaccines and long‐term follow‐up: A systematic review and meta‐analysis
Bibliographic record
Abstract
Abstract Objective Seizures have been reported as an adverse event of the COVID‐19 vaccine. However, there is no solid evidence of increased seizure occurrence compared to the general population. This study was undertaken to investigate seizure occurrence among COVID‐19 vaccine recipients compared to unvaccinated controls. Methods A systematic search was made of PubMed, Web of Science, Scopus, and Cochrane Library up to April 9, 2024. Studies reporting seizure occurrence following COVID‐19 vaccination were included. This study is reported according to the Preferred Reporting Items for Systematic Reviews and Meta‐Analyses framework and was conducted using random‐ and common‐effect models. The risk of bias in the studies was evaluated by the Newcastle–Ottawa Scale. The outcome of interest was new onset seizure incidence proportion compared among (1) COVID‐19 vaccine recipients, (2) unvaccinated cohorts, and (3) various types of COVID‐19 vaccines. Results Forty studies were included, of which seven entered the meta‐analysis. Results of the pooled analysis of the new onset seizure incidence (21‐ or 28‐day period after vaccination) in 13 016 024 vaccine recipients and 13 013 262 unvaccinated individuals by pooling the cohort studies did not show any statistically significant difference between the two groups (odds ratio [OR] = .48, 95% confidence interval [CI] = .19–1.20, p = .12, I2 = 95%, τ2 = .7145). Pooling four studies accounting for 19 769 004 mRNA versus 47 494 631 viral vector vaccine doses demonstrated no significant difference in terms of new onset seizure incidence between the groups (OR = 1.18, 95% CI = .78–1.78, p = .44, I2 = 0%, τ2 = .004). Significance This systematic review and meta‐analysis shows no statistically significant difference in the risk of new onset seizure incidence between COVID‐19 vaccinated individuals and unvaccinated individuals.
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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 | 0.012 | 0.028 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.038 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".