Interpreting the Tests of Focal Cortical Dysplasia for Epilepsy Surgery Referral
Bibliographic record
Abstract
OBJECTIVE: Focal cortical dysplasia (FCD) is a common cause of refractory, focal onset epilepsy in children. Interictal, scalp electroencephalograph (EEG) markers have been associated with these pathologies and epilepsy surgery may be an option for some patients. We aim to study how scalp EEG and magnetic resonance imaging (MRI) markers of FCD affect referral of these patients for surgical evaluation. METHODS: A single-center, retrospective review of children with focal onset epilepsy. Patients were included if they were between 1 month and 18 years of age, had focal onset seizures, prolonged scalp EEG monitoring, and an MRI conducted after 2 years of age. Statistics were carried out using the chi-squared and student's t-test, as well as a logistic regression model. RESULTS: Sixty-eight patients were included in the study. Thirty-seven of these patients were referred to a comprehensive pediatric epilepsy program (CPEP) for surgical evaluation, and of these 22% showed FCD EEG markers, 32% FCD MRI markers, and 10% had both. These markers were also present in patients not referred to a CPEP. The MRI markers were significantly associated with CPEP referral, whereas EEG markers were not. Neither marker type was associated with epilepsy surgery. CONCLUSION: This study found that children with focal onset epilepsy were more likely to be referred for surgical evaluation if they were medically refractory, or were diagnosed with FCD or tumor on MRI. Scalp EEG markers of FCD were not associated with CPEP referral. The online tool CASES may be a useful physician guide for identifying appropriate children for epilepsy surgery referral.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".