Relation of Pregnancy and Neonatal Factors to Subsequent Development of Childhood Epilepsy: A Population-Based Cohort Study
Bibliographic record
Abstract
OBJECTIVE: We examined the effect of pregnancy and neonatal factors on the subsequent development of childhood epilepsy in a population-based cohort study. PATIENTS AND METHODS: Children born between January 1986 and December 2000 in Nova Scotia, Canada were followed up to December 2001. Data on pregnancy and neonatal events and on diagnoses of childhood epilepsy were obtained through record linkage of 2 population-based databases: the Nova Scotia Atlee Perinatal Database and the Canadian Epilepsy Database and Registry. Factors analyzed included events during the prenatal, labor and delivery, and neonatal time periods. Cox proportional hazards regression models were used to estimate relative risks and 95% confidence intervals. RESULTS: There were 648 new cases of epilepsy diagnosed among 124,207 live births, for an overall rate of 63 per 100,000 person-years. Incidence rates were highest among children <1 year of age. In adjusted analyses, factors significantly associated with an increased risk of epilepsy included eclampsia, neonatal seizures, central nervous system (CNS) anomalies, placental abruption, major non-CNS anomalies, neonatal metabolic disorders, neonatal CNS diseases, previous low birth weight infant, infection in pregnancy, small for gestational age, unmarried, and not breastfeeding infant at the time of discharge from hospital. CONCLUSIONS: Our study supports the concept that prenatal factors contribute to the occurrence of subsequent childhood epilepsy.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".