The diagnostic accuracy of routine electroencephalography after a first unprovoked seizure
Bibliographic record
Abstract
The clinical utility of routine electroencephalography (EEG) after a first unprovoked seizure remains uncertain. Its diagnostic accuracy in identifying adults and children with new onset epilepsy was examined. A systematic review and meta-analysis of studies examining individuals who underwent routine EEG after a first unprovoked seizure and were followed for seizure recurrence for at least 1 year was performed. A 'positive' test was defined by the presence of epileptiform discharges (ED). Pooled sensitivity and specificity estimates were calculated using a bivariate random effects regression model. In all, 3096 records were reviewed, from which 15 studies were extracted with a total of 1799 participants. Amongst adult studies, the sensitivity and specificity (95% confidence interval) of routine EEG were 17.3% (7.9, 33.8) and 94.7% (73.7, 99.1), respectively. Amongst child studies, the pooled sensitivity and specificity were 57.8% (49.7, 65.6) and 69.6% (57.5, 79.5), respectively. Based upon our positive likelihood ratios, and assuming a pre-test probability of 50%, an adult with ED on routine EEG after a first unprovoked seizure has a 77% probability of having a second seizure, whilst a child with similar findings has a 66% probability. Further studies are required to examine the impact of patient characteristics and EEG features on the diagnostic accuracy of routine EEG for new onset epilepsy.
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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 | 0.012 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.010 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".