DIAGNOSES IN CHILDREN REFERRED TO A TERTIARY CARE, FIRST SEIZURE CLINIC
Bibliographic record
Abstract
Objectives: To determine (a) the range of diagnoses presenting to a first seizure clinic, and (b) the seizure type, syndrome, etiology and prevalence of prior seizures in those presenting with epileptic events. Methods: Between 01/01/2004 and 30/08/2005, 127 children were seen in a First Seizure Clinic at the Alberta Children's Hospital by one of two epileptologists. Inclusion criteria were age >1 month and an unprovoked event suggestive of seizure. Data collected included referring physician specialty, child's age, gender, developmental status and clinical diagnosis of epileptologist (non-epileptic vs. epileptic). For those with epileptic events, seizure type, syndrome (if identifiable), presumed etiology (idiopathic, cryptogenic, symptomatic), presence of prior afebrile and febrile seizures, presence and type of provoking factors, family history of epilepsy, pre/perinatal complications and EEG results were recorded. Results: The diagnosis was epileptic in 94 (74%), nonepileptic in 31 (24%) and unclassifiable in 2 (2%). Specialists were no more likely than generalists to refer true epileptic events (75% vs. 58%, p<0.1). Gender ratio was 67M: 60F, and mean age at presentation was 8 years. Fifteen percent of children were developmentally delayed and neurological examination was abnormal in 11%. For those diagnosed with epileptic events, 32 presented with generalized (26 PGTC, 2 absence, 4 myoclonic) while 62 presented with partial onset seizures (2 partial simple, 27 partial complex, 28 SGTC). An epilepsy syndrome was identifiable in 15 cases. 28% experienced a prior seizure, which was unrecognized in 14%. Etiology was idiopathic in 35%, cryptogenic in 44% and symptomatic in 21%. An EEG was done in all children with seizures and was abnormal in 41%. Conclusion: Nearly one quarter of children referred to a first seizure clinic had non-epileptic events. Of children presenting with true seizures, 28% had a history of a previous, often more subtle event.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".