Presence of interictal epileptiform EEG discharges implies increased risk of recurrence after the first unprovoked seizure: Report of the International League Against Epilepsy and International Federation of Clinical Neurophysiology
Bibliographic record
Abstract
Objective: A joint International Federation of Clinical Neurophysiology - International League Against Epilepsy (IFCN-ILAE) Taskforce was created to explore the published evidence for initial EEGs in the evaluation of patients who experienced their first unprovoked seizure, and to determine the diagnostic value of EEG in supporting the diagnosis of epilepsy. Methods: We conducted a systematic literature review, with two independent authors screening each study. We extracted seizure recurrence data among patients with EEG showing interictal epileptiform discharges (IEDs) versus those with normal or nonspecific-abnormal EEG results. Random-effects meta-analyses of seizure recurrence in relation to IEDs was conducted in the included studies, calculating odds ratios (OR) with confidence intervals (CI) and diagnostic accuracy. Results: A total of 4847 patients from 22 studies with variable follow-up durations were analysed. The random-effects pooled binary estimate of seizure recurrence was 47 % (95 % CI 40 %-55 %). The overall proportion with seizure recurrence was higher in patients with IEDs (60 %; 95 % CI 53 %-68 %) compared to those without (40 %; 95 % CI 33 %-48 %, p < 0.001). Random-effects meta-analysis showed that the presence of IEDs was associated with seizure recurrence (OR: 2.32, 95 % CI 1.69-3.17, p < 0.001). Subgroup analyses of adults and children showed that this difference remained significant in both groups: OR in children of 3.24 (95 % CI 2.19-4.79) and in adults of 1.55 (95 % CI 1.08-2.21). In eight studies (n = 1209, 923 children) patients remained untreated before the second seizure; the pooled probability of seizure recurrence in those with IED in these studies was no different than in studies in which some patients were treated. Significance: In conclusion, the presence of IEDs in EEG recordings obtained after the first unprovoked seizure can help clinicians to confirm the clinical diagnosis of epilepsy after a first unprovoked seizure, according to the revised ILAE definition. These results support the relevance of IED detection on EEG as a predictor of seizure recurrence after a first unprovoked seizure. However, its prognostic value is influenced by age and other clinical factors.
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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 | 0.022 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.023 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".