Scalp Ripples Can Predict Development of Epilepsy After First Unprovoked Seizure in Childhood
Bibliographic record
Abstract
Objective Identification of children at risk of developing epilepsy after a first unprovoked seizure can be challenging. Interictal epileptiform discharges are associated with higher risk but have limited sensitivity and specificity. High frequency oscillations (HFOs) are newer biomarkers for epileptogenesis. We prospectively evaluated the predictive value of HFOs for developing epilepsy in scalp electroencephalogram (EEG) of children after a first unprovoked seizure. Methods After their first seizure, 56 children were followed prospectively over 12 months and then grouped in “epilepsy” or “no epilepsy.” Initial EEGs were visually analyzed for spikes, spike ripples, and ripples. Inter‐group comparisons of spike‐rates and HFO‐rates were done by Mann–Whitney U test. Predictive values and optimal thresholds were calculated by receiver operating characteristic (ROC) curves. Results In the epilepsy group (n = 26, 46%), mean rates of ripples (0.3 vs 0.09 / minute, p < 0.0001) and spike ripples (0.6 vs 0.06 / minute, p < 0.05) were significantly higher, with no difference in spike rates (1.7 vs 3.0 / minute, p = 0.38). Of those 3 markers, ripples showed the best predictive value (area under the curve [AUC]ripples = 0.88). The optimal threshold for ripples was calculated to be ≥ 0.125 / minute with a sensitivity of 87% and specificity of 85%. Ripple rates were negatively correlated to days passing before epilepsy‐diagnosis (R = −0.59, p < 0.0001) and time to a second seizure (R = −0.64, 95% confidence interval [CI] = −0.77 to 0.43, p < 0.0001). Interpretation We could show that in a cohort of children with a first unprovoked seizure, ripples predict the development of epilepsy better than spikes or spike ripples and might be useful biomarkers in the estimation of prognosis and question of treatment. ANN NEUROL 2021;89:134–142
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 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".