Risk of seizures after immunization in children with epilepsy: a risk interval analysis
Bibliographic record
Abstract
BACKGROUND: In children with epilepsy, fever and infection can trigger seizures. Immunization can also induce inflammation and fever, which could theoretically trigger a seizure. The risk of seizure after immunization in children with pre-existing epilepsy is not known. The study objective was to determine the risk of medically attended seizure after immunization in children with epilepsy < 7 years of age. METHODS: We conducted a retrospective study of children < 7 years of age with epilepsy in Nova Scotia, Canada from 2010 to 2014. Hospitalizations, emergency visits, unscheduled clinic visits, and telephone calls for seizures were extracted from medical records. Immunization records were obtained from family physicians and Public Health with informed consent. We conducted a risk interval analysis to estimate the relative risk (RR) of seizure during risk periods 0-14, 0-2, and 5-14 days post-immunization versus a control period 21-83 days post-immunization. RESULTS: There were 302 children with epilepsy who were eligible for the study. Immunization records were retrieved on 147 patients (49%), of whom 80 (54%) had one or more immunizations between the epilepsy diagnosis date and age 7 years. These 80 children had 161 immunization visits and 197 medically attended seizures. Children with immunizations had more seizures than either those with no immunizations or those with no records (mean 2.5 versus 0.7 versus 0.9, p < 0.001). The risk of medically attended seizure was not increased 0-14 days after any vaccine (RR = 1.1, 95% confidence interval (CI): 0.5-2.8) or 0-2 days after inactivated vaccines (RR = 0.9, 95% CI: 0.1-7.1) versus 21-83 days post-immunization. No seizure events occurred 5-14 days after live vaccines. CONCLUSIONS: Children with epilepsy do not appear to be at increased risk of medically attended seizure following immunization. These findings suggest that immunization is safe in children with epilepsy, with benefits outweighing risks.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".