Paroxysmal cortical slowing linked to drug-resistant epilepsy
Bibliographic record
Abstract
BACKGROUND: Paroxysmal slow wave events (PSWEs), defined as electroencephalography (EEG) segments where the median power frequency falls below 6 Hz for ≥5 s, have been shown to predict epilepsy in patients with a first seizure. We evaluated the prevalence and localisation of PSWEs in large independent EEG datasets, exploring their potential as biomarkers for drug-resistant epilepsy (DRE). METHODS: An exploratory analysis used 1064 participants from the Temple University EEG corpus, comparing patients with epilepsy (N = 903) with participants with seizure-mimics and normal EEGs (N = 161). Validation analysis used an independent cohort from Bonn University, comprising drug-responsive (N = 51) and patients with DRE (N = 44). FINDINGS: In the exploratory analysis, the proportion of time PSWEs were detected was longer in epilepsy compared with participants without epilepsy (P < 0.0001). Analyses of aetiology and EEG localisation revealed that PSWEs were most prolonged in patients with reported focal epilepsy (P = 0.004), particularly with temporal lobe involvement (P = 0.005). Patients with DRE had prolonged time in PSWEs (P = 0.005), corresponding with an increased risk of refractoriness (OR = 1.9; 95% CI 1.2-2.9). Validation analysis confirmed these findings, with prolonged PSWEs in DRE vs. drug-responsive patients (P < 0.0001, AUC = 0.829). Based on the cutoff established in the exploratory cohort, prolonged time in PSWEs in the validation cohort was associated with increased DRE risk (OR = 5.14, 95% CI 2.1-12.3). In patients with poor surgical outcomes (Engel IB-IV, N = 13), pre-surgical EEGs showed prolonged time in PSWEs compared with Engel IA (N = 24, P = 0.038). INTERPRETATION: Analysis of 1159 EEGs from two independent cohorts demonstrated that PSWEs are more prevalent and prolonged in patients with focal epilepsy and may indicate a lack of therapeutic response. FUNDING: The Canadian Institutes of Health Research (168164, 180636).
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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 | 0.001 |
| 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) | 0.000 | 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".