Value of the Early Electroencephalogram after a First Unprovoked Seizure
Bibliographic record
Abstract
Studies on the predictive value of the electroencephalogram (EEG) concerning the risk of seizure recurrence have shown contradictory results. We prospectively studied the predictive value of the standard EEG and EEG with sleep deprivation for seizure relapse in adult patients presenting with a first unprovoked seizure. EEGs were performed on 157 adult patients within the first 48 hours of the first seizure. Additional EEGs with sleep deprivation were obtained in 60 cases. The standard EEG was abnormal in 70.7% and significantly associated with an increased risk of seizure recurrence [risk ratio 4.5, 95% confidence interval (CI) 1.8; 11.3, p=0.001]. Subgroup analysis revealed the highest recurrence rates for patients with focal epileptiform activity (risk ratio 2.2, CI 1.2; 4.2, p=0.01). EEGs with sleep deprivation were abnormal in 48.3% of all cases and revealed epileptiform discharges in 13.3% of the patients who had no epileptiform activity in the standard EEG. Routine EEG revealed abnormalities in 60 of 94 patients who presented with normal neurologic status on admission. Further neuroradiological examinations detected previously unknown brain lesions in 19 of these cases, particularly cerebrovascular disease (CVD, n=7), brain tumors (n=6), and posttraumatic scars (n=4). In conclusion, the EEG is important for the early detection of focal nonepileptic and epileptic abnormalities after a first unprovoked seizure in adult patients and may provide valuable information on previously unknown disorders, particularly CVD and cerebral tumors. The abnormal EEG is a highly significant predictor for seizure recurrence. An additional EEG with sleep deprivation is helpful in cases when standard EEG does not reveal epileptiform discharges.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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".