Electroclinical features of absence seizures in childhood absence epilepsy
Bibliographic record
Abstract
OBJECTIVE: To accurately define the electroclinical features of absence seizures in children with newly diagnosed, untreated childhood absence epilepsy (CAE). METHODS: The authors searched an EEG database for absence seizures in normal children with new onset untreated absence epilepsy. Seventy consecutive children were classified into IGE syndromes. The clinical and EEG features of the seizures in the children with CAE were analyzed using video-EEG recordings. RESULTS: The authors analyzed 339 absence seizures in 47 children with CAE. The average seizure duration was 9.4 seconds and clinical features consisted of arrest of activity, loss of awareness, staring, and 3-Hz eyelid movements, but there was individual variation. Ictal EEG predominantly showed regular 3-Hz generalized spike and wave (GSW) with one or two spikes per wave; however, disorganization of discharges was common and three or more spikes per wave occurred rarely. Postictal slowing was frequent. Interictal abnormalities included fragments of GSW, posterior bilateral delta activity, and focal discharges. Although all 47 children met the current criteria for CAE, only 5 fulfilled the recently proposed criteria for CAE. CONCLUSION: The heterogeneous nature of each clinical and EEG feature of untreated absence seizures is of critical importance when determining criteria for childhood absence epilepsy.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".