Quantitative Interictal Subdural EEG Analyses in Children with Neocortical Epilepsy
Bibliographic record
Abstract
PURPOSE: We studied the relation between quantitative interictal subdural EEG data and visually defined ictal subdural EEG findings in children with intractable neocortical epilepsy, and determined whether interictal EEG data are predictive of ictal EEG onset zones. METHODS: Thirteen children (aged 1.2-15.4 years) underwent prolonged intracranial EEG recording, using 48- to 120-channel subdural electrodes. Three distinct 10-min segments of the continuous interictal EEG recording were selected for each patient, and the spike frequency for each channel was determined by using an automatic spike-detection program. Subsequently the average spike frequency of each electrode was compared with ictal assessment (onset, spread, and no early ictal involvement). In addition, 50 distinct interictal spikes were averaged for each patient, and the amplitude and latency after the leading spike (averaged spike showing the earliest peak) were measured for each electrode and analyzed with respect to ictal EEG findings. RESULTS: Reproducibility of the spike-frequency pattern derived from three 10-min segments was high (Kendall's W, 0.85 +/- 0.08). Electrodes showing the highest spike frequency, the highest spike amplitude, and the leading spike were found to be a part of the seizure onset in 13 of 13, 12 of 13, and 10 of 13 cases, respectively. There was significant correlation between ictal assessment and spike frequency as well as spike amplitude. A receiver operating characteristics analysis showed that a cutoff threshold at 14% of the maximal spike frequency resulted in a specificity of 0.90 and a sensitivity of 0.77 for the detection of seizure-onset electrodes. CONCLUSIONS: Quantitative interictal subdural EEG may predict ictal-onset zones in children with intractable neocortical 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 | 0.000 | 0.000 |
| 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) | 0.001 | 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 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".