Period Prevalence of Epilepsy in Children in BC: A Population-Based Study
Bibliographic record
Abstract
BACKGROUND: Most estimates of the prevalence of seizure disorders in Canada derive from national surveys which differ in sampling and case-finding methods. This study used health care utilization data to make a population-based estimate of the prevalence of epileptic seizures and of epilepsy in children in British Columbia (BC). METHODS: All BC residents between 0-19 years-of-age in 2002-3 enrolled in the Medical Services Plan were included. Epileptic seizures were defined using ICD-9 codes; health care utilization data was obtained from BC Linked Health Database. The period prevalence of epileptic seizures and of epilepsy was determined by age, urban/rural region and socioeconomic status. RESULTS: 8,125 of 1,013,816 children were identified as having an epileptic seizure of which 5621 were classified as epilepsy--5.5 per 1000 children (95% CI: 5.4-5.7). The prevalence of epilepsy in infants and preschoolers was higher than that reported in the literature. A higher prevalence of epilepsy was observed also among those with low socioeconomic status. A higher prevalence of epilepsy was observed in those health regions with a higher proportion of First Nations and a lower prevalence was observed in health regions with a higher proportion of visible minorities. CONCLUSIONS: Age-specific prevalence rates in BC children for epilepsy, determined from population-based administrative records, were similar to published data except in children under five years. We found a gradient of increased prevalence with decreased level of income. Prevalence rates based on utilization data have the potential to guide program planning for children with epileptic 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| 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".