Prevalence and etiology of epilepsy in a <scp>N</scp>orwegian county—A population based study
Bibliographic record
Abstract
OBJECTIVE: Epilepsy represents a substantial personal and social burden worldwide. When addressing the multifaceted issues of epilepsy care, updated epidemiologic studies using recent guidelines are essential. The aim of this study was to find the prevalence and causes of epilepsy in a representative Norwegian county, implementing the new guidelines and terminology suggested by the International League Against Epilepsy (ILAE). METHODS: Included in the study were all patients from Buskerud County in Norway with a diagnosis of epilepsy at Drammen Hospital and the National Center for Epilepsy at Oslo University Hospital. The study period was 1999-2014. Patients with active epilepsy were identified through a systematic review of medical records, containing information about case history, electroencephalography (EEG), cerebral magnetic resonance imaging (MRI), genetic tests, blood samples, treatment, and other investigations. Epilepsies were classified according to the revised terminology suggested by the ILAE in 2010. RESULTS: In a population of 272,228 inhabitants, 1,771 persons had active epilepsy. Point prevalence on January 1, 2014 was 0.65%. Of the subjects registered with a diagnostic code of epilepsy, 20% did not fulfill the ILAE criteria of the diagnosis. Epilepsy etiology was structural-metabolic in 43%, genetic/presumed genetic in 20%, and unknown in 32%. Due to lack of information, etiology could not be determined in 4%. SIGNIFICANCE: Epilepsy is a common disorder, affecting 0.65% of the subjects in this cohort. Every fifth subject registered with a diagnosis of epilepsy was misdiagnosed. In those with a reliable epilepsy diagnosis, every third patient had an unknown etiology. Future advances in genetic research will probably lead to an increased identification of genetic and hopefully treatable causes of epilepsy.
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How this classification was reachedexpand
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 | 0.001 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".