Utility of magnetic source imaging in nonlesional focal epilepsy: a prospective study
Bibliographic record
Abstract
OBJECTIVE: For patients with nonlesional refractory focal epilepsy (NLRFE), localization of the epileptogenic zone may be more arduous than for other types of epilepsy and frequently requires information from multiple noninvasive presurgical modalities and intracranial EEG (icEEG). In this prospective, blinded study, the authors assessed the clinical added value of magnetic source imaging (MSI) in the presurgical evaluation of patients with NLRFE. METHODS: This study prospectively included 57 consecutive patients with NLRFE who were considered for epilepsy surgery. All patients underwent noninvasive presurgical evaluation and then MSI. To determine the surgical plan, discussion of the results of the presurgical evaluation was first undertaken while discussion participants were blinded to the MSI results. MSI results were then presented. MSI influence on the initial management plan was assessed. RESULTS: MSI results influenced patient management in 32 patients. MSI results led to the following changes in surgical strategy in 14 patients (25%): allowing direct surgery in 6 patients through facilitating the detection of subtle cortical dysplasia in 4 patients and providing additional concordant diagnostic information to other presurgical workup in another 2 patients; rejection of surgery in 3 patients originally deemed surgical candidates; change of plan from direct surgery to icEEG in 2 patients; and allowing icEEG in 3 patients deemed not surgical candidates. MSI results led to changed electrode locations and contact numbers in another 18 patients. Epilepsy surgery was performed in 26 patients influenced by MSI results and good surgical outcome was achieved in 21 patients. CONCLUSIONS: This prospective, blinded study showed that information provided by MSI allows more informed icEEG planning and surgical outcome in a significant percentage of patients with NLRFE and should be included in the presurgical workup in those patients.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".