Incidence, prevalence and aetiology of seizures and epilepsy in children
Bibliographic record
Abstract
AIM: To (1) summarize published, peer-reviewed literature about the incidence and prevalence of epilepsy in children from developed and developing countries around the world, and (2) discuss problems in defining aetiologies of epilepsy in children, and distinguish between seizures and epilepsy. METHODS: Review of selected literature with particular attention to systematic reviews. RESULTS: The incidence of epilepsy in children ranges from 41-187/100,000. Higher incidence is reported from underdeveloped countries, particularly from rural areas. The incidence is consistently reported to be highest in the first year of life and declines to adult levels by the end of the first decade. The prevalence of epilepsy in children is consistently higher than the incidence and ranges from 3.2-5.5/1,000 in developed countries and 3.6-44/1,000 in underdeveloped countries. Prevalence also seems highest in rural areas. The incidence and prevalence of specific seizure types and epilepsy syndromes is less well documented. In population-based studies, there is a slight, but consistent, predominance of focal seizures compared with generalized seizures. Only about one third of children with epilepsy can be assigned to a specific epilepsy syndrome, as defined by the most recently proposed system for organization of epilepsy syndromes. CONCLUSIONS: The incidence and prevalence of epilepsy in children appears to be lower in developed countries and highest in rural areas of underdeveloped countries. The reasons for these trends are not well established. Although focal seizures predominate, the incidence and prevalence of specific epilepsy syndromes is not well documented.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".