Bibliographic record
Abstract
EEG remains the primary diagnostic tool for evaluating seizures in children, with interictal epileptiform discharges (IEDs) serving as key biomarkers of epileptogenic activity. However, not all IEDs have the same prognostic significance. Variations in IED topography, morphology, frequency, and timing influence outcomes in pediatric epilepsy. The developing brain's maturation affects IED location and features, creating age-specific patterns with distinct implications. For example, occipital and midline IEDs are common in young children, with midline IEDs strongly linked to increased seizures and developmental delay than control patients. Morphological features provide additional prognostic stratification. While centrotemporal IEDs with tangential dipoles are well-established as favorable prognostic markers, IEDs exhibiting tangential dipoles in any brain region are associated with more benign clinical courses than control patients. Conversely, positive sharp waves persisting beyond the neonatal period signal less favorable prognosis, including developmental delay, abnormal neurological examination, and structural brain abnormalities. Additionally, IEDs occurring on ripples have been shown to serve as more reliable interictal biomarkers of the epileptogenic zone than IEDs or ripples alone. Topography, frequency and sleep-state dependence also carry clinical significance, as frequent IEDs during slow-wave sleep may impact cognition. Furthermore, the temporal context of IED occurrence during seizure onset, treatment, activation procedures, medication withdrawal, or after epilepsy surgery provides valuable prognostic information. Recognition of these nuanced electrophysiological distinctions enhances clinicians' ability to predict clinical trajectories and optimize long-term management strategies.
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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 | 0.001 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.115 | 0.079 |
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".