SECONDARY EPILEPTIC FOCI IN CHILDREN WITH INTRACTABLE EPILEPSY SECONDARY TO CORTICAL DYSPLASIAS
Bibliographic record
Abstract
Objectives: Cortical dysplasias are intrinsically epileptogenic. Long and intractable seizure histories in childhood secondary to the cortical dysplasia provoke secondary epileptic foci. We evaluated spatial and temporal profiles and identify risk factors of secondary epileptogenesis Methods: We retrospectively studied 34 children with intractable localization-related epilepsy with histopathological confirmed cortical dysplasia. We reviewed seizure histories and semiologies, MR images, serial EEGs, video EEGs (VEEGs) and MEG findings. We defined secondary epileptic foci as new ictal onsets, remote and independent interictal discharges on subsequent EEGs, and new dipole locations on subsequent MEGs. We evaluated demographic variables, histopathology and secondary epileptic foci correlating with surgical outcomes according to Engel. Results: We found secondary epileptic foci in 25 (74%) of 34 patients. They had new ictal onsets on VEEG in 5 (15%), secondary epileptic interictal discharges in 16 (47%), secondary clustered MEG dipoles in 5 (15%) scattered MEG dipoles in 17(50%). Fifteen (60%) of 25 patients with secondary epileptic foci and 4 (45%) of 9 patients without secondary epileptic foci became seizure free, Engel Ia. There was no significant difference in seizure outcome between the 2 groups of patients with/without secondary epileptic foci. Conclusion: Secondary epileptic foci on VEEG and/or MEG did not necessarily correlate with a poor surgical outcome in patients with intractable localization-related epilepsy due to cortical dysplasias. Remaining seizures may be attributed to the residual epileptogenic zone around the resection area rather than secondary epileptic foci.
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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 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".