Yield of epileptiform electroencephalogram abnormalities in incident unprovoked seizures: A population‐based study
Bibliographic record
Abstract
OBJECTIVE: The yield of epileptiform abnormalities in serial electroencephalography (EEG) studies has not been addressed in a population-based setting for subjects with incident epilepsy or a single unprovoked seizure, raising the possibility of methodologic limitations such as selection bias. Our aim was to address these limitations by assessing the yield and predictors of epileptiform abnormalities for the first and subsequent EEG recording in a study of incident epilepsy or single unprovoked seizure in Rochester, Minnesota. METHODS: We used the resources of the Rochester Epidemiology Project to identify all 619 residents of Rochester, Minnesota, born in 1920 or later with a diagnosis of incident epilepsy (n = 478) or single unprovoked seizure (n = 141) between 1960 and 1994, who had at least one EEG study. Information on all EEG studies and their results was obtained by comprehensive review of medical records. RESULTS: Among subjects with epilepsy, the cumulative yield of epileptiform abnormalities was 53% after the first EEG study and 72% after the third. Among subjects with a single unprovoked seizure, the cumulative yield was 39% after the first EEG study and 68% after the third. Young age at diagnosis and idiopathic etiology were risk factors for finding epileptiform abnormalities across all EEG recordings. SIGNIFICANCE: Although the cumulative yield of epileptiform abnormalities increases over successive EEG recordings, there is a decrease in the increment for each additional EEG study after the first EEG study. This is most evident in incident epilepsy and in younger subjects. Clinically it may be worthwhile to consider that the probability of finding an epileptiform abnormality after the third nonepileptiform EEG recording is low.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".