Technical Aspects of SEEG and Its Interpretation in the Delineation of the Epileptogenic Zone
Bibliographic record
Abstract
Stereo-electroencephalography (SEEG) has gained global popularity in recent years. In Japan, a country in which invasive studies using subdural electrodes (SDEs) have been the mainstream, SEEG has been approved for insurance coverage in 2020 and is expected to gain in popularity. Some concepts supporting SEEG methodology are fundamentally different from that of SDE studies. Clinicians interested in utilizing SEEG in their practice should be aware of those aspects in which they differ. Success in utilizing the SEEG methodology relies heavily on the construction of an a priori hypothesis regarding the putative seizure onset zone (SOZ) and propagation. This article covers the technical and theoretical aspects of SEEG, including the surgical techniques and precautions, hypothesis construction, and the interpretation of the recording, all with the aim of providing an introductory guide to SEEG.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| 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".