Childhood seizures after prenatal exposure to maternal influenza infection: a population-based cohort study from Norway, Australia and Canada
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.000 | 0.000 |
| 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 it