The role of magnetoencephalography in epilepsy surgery
Bibliographic record
Abstract
Epilepsy surgery requires the precise localization of the epileptogenic zone and the anatomical localization of eloquent cortex so that these areas can be preserved during cortical resection. Magnetoencephalography (MEG) is a technique that maps interictal magnetic dipole sources onto MR imaging to produce a magnetic source image. Magneto-encephalographic spike sources can be used to localize the epileptogenic zone and be part of the workup of the patient for epilepsy surgery in conjunction with data derived from an analysis of seizure semiology, scalp video electroencephalography, PET, functional MR imaging, and neuropsychological testing. In addition, magnetoencephalographic spike sources can be linked to neuronavigation platforms for use in the neurosurgical field. Finally, paradigms have been developed so that MEG can be used to identify functional areas of the cerebral cortex including the somatosensory, motor, language, and visual evoked fields. The authors review the basic principles of MEG and the utility of MEG for presurgical planning as well as intra-operative mapping and discuss future applications of MEG technology.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".