Diagnostic Inaccuracy in Children Referred with “First Seizure”: Role for a First Seizure Clinic
Bibliographic record
Abstract
PURPOSE: To determine (a) the range of diagnoses, and (b) the prevalence of previous seizures in children presenting to a first seizure clinic. METHODS: One hundred twenty-seven children were seen in a tertiary care First Seizure Clinic. Inclusion criteria were age 1 month-17 years with an unprovoked event suggestive of seizure. Data collected included referring physician specialty, child's age, gender, developmental status, and clinical diagnosis of epileptologist (nonepileptic vs. epileptic). For those with epileptic events, seizure type, syndrome (if identifiable), presumed etiology (idiopathic, cryptogenic, and symptomatic), presence of prior afebrile and febrile seizures, provoking factors, family history, pre/perinatal complications and EEG results were recorded. RESULTS: The diagnosis was epileptic in 94 (74%), nonepileptic in 31 (24%) and unclassifiable in two (2%). Pediatricians were more likely to refer true epileptic events (92%) than ED physicians (76%) or family physicians (65%). 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 while 62 presented with partial onset seizures. An epilepsy syndrome was identifiable in 15 cases. Thirty-eight percent experienced a prior probable seizure which was recognized by the referring physician in only one case. An EEG was done in all children with seizures and was abnormal in 41%. Early EEG was performed in 20% of children and did not show statistical significance. CONCLUSIONS: Diagnostic inaccuracy is common in first seizure. One quarter of children were incorrectly diagnosed as having a seizure while the diagnosis of epilepsy was missed in over one-third of children.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".