Clinical MEG Analyses for Children with Intractable Epilepsy
Bibliographic record
Abstract
Epilepsy is a brain disorder characterized by recurrent and unpredictable interruptions of normal brain function, called epileptic seizures (Fisher et al., 2005). Epilepsy occurs in 1-2% of children (Hauser & Kurland, 1975). Twenty-five percent of children with epilepsy continue to seize despite appropriate medical management and are diagnosed as medically refractory epilepsy (Hauser, 1993). Medically refractory epilepsy is defined as seizures that continue despite at least two appropriate first line anti-epileptic medications at maximally tolerated serum levels for 2 years (Snead, 2001). A subset of patients with medically refractory epilepsy can be surgically treated after anti-epileptic medications fail. Successful control of the seizures can be achieved in children with intractable partial epilepsy by surgical resection of the epileptogenic foci (Duchowny 1995; Wyllie 1998; Snead 2001). In our institution, magnetoencephalography (MEG) is an essential part of the diagnostic workup in all patients undergoing presurgical evaluation. We introduced the concept of MEG-guided epilepsy surgery (Minassian et el., 1999; Otsubo et al., 1999, 2001a, 2001b; Holowka et al., 2004; Iida et al., 2005; RamachandranNair et al., 2007; Mohamed et al., 2007; Ochi et al., 2008). In patients with intractable partial epilepsy, MEG is a powerful tool for presurgical evaluation to predict an epileptogenic zone (Wheless et al., 1999; Pataraia et al., 2004; Paulini et al., 2007). The epileptogenic zone is a region of cortex that can generate epileptic seizures (Rosenow & Luders, 2001). By definition, total removal or disconnection of the epileptogenic zone is necessary and sufficient for seizure freedom. In this chapter, we would like to demonstrate our presurgical evaluation, including scalp videoelectroencephalography (EEG) monitoring to capture seizures and interictal epileptiform discharges, and clinical MEG studies and analyses to estimate the epileptogenic zone.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".