Which is more deleterious to cognitive performance? Interictal epileptiform discharges vs anti‐seizure medication
Bibliographic record
Abstract
Children with epilepsy commonly have comorbid neurocognitive impairments that severely affect their psychosocial well-being, education, and future career prospects. Although the provenance of these deficits is multifactorial, the effects of interictal epileptiform discharges (IEDs) and anti-seizure medications (ASMs) are thought to be particularly severe. Although certain ASMs can be leveraged to inhibit IED occurrence, it remains unclear whether epileptiform discharges or the medications themselves are most deleterious to cognition. To examine this question, 25 children undergoing invasive monitoring for refractory focal epilepsy performed one or more sessions of a cognitive flexibility task. Electrophysiological data were recorded to detect IEDs. Between repeated sessions, prescribed ASMs were either continued or titrated to <50% of the baseline dose. Hierarchical mixed-effects modeling assessed the relationship between task reaction time (RT), IED occurrence, ASM type, and dose while controlling for seizure frequency. Both presence (β ± SE = 49.91 ± 16.55 ms, p = .003) and number of IEDs (β ± SE = 49.84 ± 12.51 ms, p < .001) were associated with slowed task RT. Higher dose oxcarbazepine significantly reduced IED frequency (p = .009) and improved task performance (β ± SE = -107.43 ± 39.54 ms, p = .007). These results emphasize the neurocognitive consequences of IEDs independent of seizure effects. Furthermore, we demonstrate that inhibition of IEDs following treatment with select ASMs is associated with improved neurocognitive function.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".