Prognostic value of interictal epileptiform discharges on routine <scp>EEG</scp> in adults with epilepsy
Bibliographic record
Abstract
OBJECTIVE: To determine whether interictal epileptiform discharges (IEDs) on routine electroencephalography (EEG) predict seizure recurrence in adults with established epilepsy. METHODS: We conducted a retrospective survival analysis of consecutive adults with epilepsy undergoing routine EEG at a tertiary center between 2018 and 2019. Using multivariate Cox proportional hazards models guided by a directed acyclic graph and adjusted for confounders including past seizure frequency and duration of epilepsy, we estimated the association between the presence of IEDs and time to next seizure, stratified by epilepsy type. RESULTS: We included 488 consecutive routine EEG studies from 438 patients. Over a median follow-up of 124.5 weeks, seizures recurred in 50.4% of cases. The presence of IEDs was associated with increased seizure risk in both focal (adjusted hazard ratio [aHR] = 1.47, 95% confidence interval [CI]: 1.01-2.15, p = .043) and generalized epilepsy (aHR = 1.82, 95% CI: 1.08-3.06, p = .024). SIGNIFICANCE: IEDs on routine EEG independently predict increased seizure risk in adults with epilepsy, with a stronger effect in generalized epilepsies. This suggests that routine EEG may have prognostic value during epilepsy follow-up and warrant further investigations as a potential prognostic biomarker to help inform clinical decision-making.
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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 | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".