Risk of febrile seizures after first dose of measles–mumps–rubella–varicella vaccine: a population-based cohort study
Bibliographic record
Abstract
BACKGROUND: The combination measles-mumps-rubella-varicella (MMRV) vaccine currently used in Canada (Priorix-Tetra) may increase the risk of febrile seizures relative to the separate vaccines (MMR and varicella) previously administered. We determined the risk of febrile seizure after the first dose of MMRV, as well as any additional risk for children at high risk for seizures because of pre-existing medical conditions. METHODS: In this retrospective, population-based cohort study, we compared the risk of seizures after the first dose of MMRV with the risk after same-day administration of separate MMR and varicella vaccines (MMR+V) in children 12 to 23 months of age in the province of Alberta. We deterministically linked vaccination data to health service utilization data for seizures. We used Poisson regression, with adjustment for age and calendar year, to determine the risk for the full cohort and for high-risk children. RESULTS: The risk of seizures 7 to 10 days after vaccination was twice as high with MMRV as with MMR+V (relative risk [RR] 1.99, 95% confidence interval [CI] 1.30-3.05). The excess absolute risk of seizures was 3.52 seizures per 10 000 doses of MMRV relative to MMR+V. In high-risk children, the risk was not differentially higher for MMRV (RR 1.30, 95% CI 0.60-2.79). INTERPRETATION: Despite an increased risk of febrile seizures following MMRV (compared with MMR+V), the absolute level of risk was small. Policy-makers need to balance these findings with the potential benefits of administering the combination vaccine or determine whether the choice of vaccine rests with clinicians and/or parents.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".