SECONDARY EPILEPTOGENESIS IN PATIENTS WITH LOCALIZATION RELATED EPILEPSY SECONDARY TO HISTOPATHOLOGICALLY PROVEN CORTICAL DYSPLASIAS
Bibliographic record
Abstract
Objectives: The cortical dysplasias are intrinsically epileptogenic and secondary epileptogenesis may alter surgical prognosis. We sought to determine the spatial and temporal profile of secondary epileptogenesis and identify risk factors for secondary epileptogenesis. Methods: We retrospectively studied 34 children with intractable localization related epilepsy with histopathological confirmed cortical dysplasia. We reviewed their seizure semiology, MRI, serial EEG/VEEG and MEG findings on presurgical evaluation. We correlated demographic variables and histopathology with surgical outcomes according to Engel. We defined secondary epileptogenesis based on a new ictal onset or remote and independent interictal discharges on subsequent VEEG, and/or a new cluster or scatter on subsequent MEG studies. Results: We found secondary epileptogenesis in 74% (25/34) of our patients most commonly in the frontal lobe. A=5 year seizure history had an attributable risk of 56% of secondary epileptogenesis. Age at seizure onset, number of AEDs, age at surgery and grades of dysplasia did not constitute significant risk factors. Conclusion: Secondary epileptogenesis did not necessarily predict a poor surgical outcome with high and stringent concordance on presurgical evaluation. These findings lend strong clinical evidence to the phenomenon of kindling and may have implications on the timing of epilepsy surgery in cortical dysplasias.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".