The Burden of Seizures in Manitoba Children: A Population-Based Study
Bibliographic record
Abstract
BACKGROUND: Population-based studies are necessary to better understand the risk factors for developing seizure disorders and the impact of these conditions on children. We undertook an assessment of the prevalence of seizure disorders in a population of children on the basis of health care utilization records. METHODS: Using Manitoba's population-based prescription and health care data for 1998/99, the prevalence of children with seizure disorders, on the basis of at least one physician visit or hospitalization for epilepsy or a prescription for an antiepileptic drug, was determined by age, urban/rural region and socioeconomic status. The latter was measured as neighbourhoods stratified by income quintiles according to Census data. RESULTS: Age-specific prevalence rates for seizure disorders in Manitoba children, determined from health care administrative records, were similar to published data on the prevalence of epilepsy, with one exception. Prevalence rates in adolescents were higher than those reported in the literature. No statistically significant differences in prevalence rates were observed between urban and rural populations. However, a higher prevalence was found among children of all ages living in lower socioeconomic neighbourhoods in urban areas, which presented as a gradient of increased prevalence with decreased levels of income. CONCLUSIONS: Population-based health care administrative data can be used to describe the geographical distribution of seizure disorders. Our data suggest that the burden of seizure disorders is not evenly distributed among children.
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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 | 0.000 | 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 | 0.001 |
| 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".