Adverse Events Following Immunization Among Children With Epilepsy
Bibliographic record
Abstract
BACKGROUND: In children with epilepsy, fever and illness are known triggers for seizure; therefore, clinicians and parents could be concerned that immunization-induced inflammation and fever could also trigger seizures. We sought to estimate the risk of emergency department (ED) visit or hospitalization for epilepsy/seizure and all causes after immunization in children younger than 7 years of age with epilepsy. METHODS: We conducted a self-controlled case series of children diagnosed with epilepsy before their 7th birthday and immunized from 2005 to 2015 in Ontario (population 14.2 million) and Manitoba (population 1.3 million), Canada, using administrative healthcare data. We estimated the age- and season-adjusted relative incidence (aRI) of epilepsy/seizure-related and all-cause ED visits/hospitalizations during various risk periods 0-28 days after inactivated and live immunizations versus a control period 35-83 days postimmunization. Estimates from each province were analyzed separately and then combined in a random-effects meta-analysis. RESULTS: The combined risk of epilepsy/seizure-related hospitalization/ED visit was increased 0-2 days after inactivated vaccines (aRI = 1.5, 95% confidence interval: 1.1-1.9) and 7-10 days after live vaccines (aRI = 1.9, 1.4-2.7). For all-cause ED visit/hospitalization, the combined aRI estimate was 0.9 (0.8-1.2) 0-2 days after inactivated vaccines and 1.3 (1.1-1.5) 7-10 days after live vaccines. CONCLUSIONS: The risk of epilepsy/seizure-related ED visit/hospitalization was modestly increased among children with epilepsy during peak periods of fever and inflammation following inactivated and live vaccines. These risks must be balanced against the risk of complications from vaccine-preventable diseases.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.009 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".