Limited role for routine EEG in the assessment of staring in children with autism spectrum disorder
Bibliographic record
Abstract
AIM: The assessment of staring episodes in children with autism spectrum disorder (ASD) is difficult due to the range of diagnostic possibilities, the increased frequency of epileptiform activity on electroencephalogram (EEG), and the inability of normal EEG to exclude seizures. We reviewed the diagnostic use of routine EEG in this setting. METHOD: The routine EEG database of the Royal Children's Hospital, Melbourne was searched for recordings during 2005-2010 in children with ASD below 16 years of age who were referred for staring. EEG reports and recordings were reviewed and epileptiform activity was characterised. RESULTS: Ninety-two EEGs in children with ASD were requested for episodes of staring. No child had absence or focal dyscognitive seizures confirmed on EEG. Findings were normal or showed non-epileptiform abnormalities in 80 children. Interictal epileptiform abnormalities were recorded in 12 children, but were judged potentially significant in only three. Seven children had epileptiform activity typical of benign focal epilepsy of childhood, such discharges seen not uncommonly in developmentally normal and delayed children without seizures. INTERPRETATION: Given the difficulties of performing EEG in children with ASD, the low yield of positive diagnostic findings and the high frequency of insignificant abnormalities, we suggest that EEG should be undertaken judiciously when evaluating children with ASD and staring episodes.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| 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".