Diagnostic utility of specific abnormal EEG patterns in children for determining epilepsy phenotype and presence of structural brain abnormalities
Bibliographic record
Abstract
Objective: Estimate sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) of EEG findings: centrotemporal spikes, photoparoxysmal response, asymmetric photic driving, and asymmetric sleep spindles, for epilepsy phenotype and presence of structural brain abnormalities. Methods: In this case-control study we reviewed children referred for EEG over a 4-year period, with at least one of centrotemporal spikes, photoparoxysmal response, asymmetric photic driving, or asymmetric sleep spindles. This cohort was analyzed in combination with a research database of pediatric patients with seizures. Results: Centrotemporal spikes had 100% sensitivity for childhood epilepsy with centrotemporal spikes or atypical childhood epilepsy with centrotemporal spikes, but lower specificity (70%) and PPV (58%). Photoparoxysmal response had high specificity (92%) and NPV (92%) for genetic generalized epilepsy. Asymmetric photic driving had low sensitivity for structural brain abnormalities (17%), with specificity 80%. In contrast, asymmetric sleep spindles had much higher sensitivity and specificity, 44% and 97%, respectively. Conclusions: Although centrotemporal spikes are classically associated with childhood epilepsy with centrotemporal spikes, these discharges are seen in other conditions. Photoparoxysmal response is highly indicative of a genetic generalized epilepsy, though may be seen in other epilepsy phenotypes. Relative attenuation of sleep spindles is a more reliable indicator of structural brain malformation than asymmetric photic driving. Significance: The quantitative diagnostic utility of EEG findings should be considered when incorporating these results into 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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".