Childhood seizures after prenatal exposure to maternal influenza infection: a population-based cohort study from Norway, Australia and Canada
Bibliographic record
Abstract
OBJECTIVE: To assess whether clinical and/or laboratory-confirmed diagnosis of maternal influenza during pregnancy increases the risk of seizures in early childhood. DESIGN: Analysis of prospectively collected registry data for children born between 2009 and 2013 in three high-income countries. We used Cox regression to estimate country-level adjusted HRs (aHRs); fixed-effects meta-analyses were used to pool adjusted estimates. SETTING: Population-based. PARTICIPANTS: 1 360 629 children born between 1 January 2009 and 31 December 2013 in Norway, Australia (New South Wales) and Canada (Ontario). EXPOSURE: Clinical and/or laboratory-confirmed diagnosis of maternal influenza infection during pregnancy. MAIN OUTCOME MEASURES: We extracted data on recorded seizure diagnosis in secondary/specialist healthcare between birth and up to 7 years of age; additional analyses were performed for the specific seizure outcomes 'epilepsy' and 'febrile seizures'. RESULTS: Among 1 360 629 children in the study population, 14 280 (1.0%) were exposed to maternal influenza in utero. Exposed children were at increased risk of seizures (aHR 1.17, 95% CI 1.07 to 1.28), and also febrile seizures (aHR 1.20, 95% CI 1.07 to 1.34). There was no strong evidence of an increased risk of epilepsy (aHR 1.07, 95% CI 0.81 to 1.41). Risk estimates for seizures were higher after influenza infection during the second and third trimester than for first trimester. CONCLUSIONS: In this large international study, prenatal exposure to influenza infection was associated with increased risk of childhood seizures.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".