Diagnostic utility of invasive<scp>EEG</scp>for epilepsy surgery: Indications, modalities, and techniques
Bibliographic record
Abstract
Many patients with medically refractory epilepsy now undergo successful surgery based on noninvasive diagnostic information, but intracranial electroencephalography (IEEG) continues to be used as increasingly complex cases are considered surgical candidates. The indications for IEEG and the modalities employed vary across epilepsy surgical centers; each modality has its advantages and limitations. IEEG can be performed in the same intraoperative setting, that is, intraoperative electrocorticography, or through an independent implantation procedure with chronic extraoperative recordings; the latter are not only resource intensive but also carry risk. A lack of understanding of IEEG limitations predisposes to data misinterpretation that can lead to denying surgery when indicated or, worse yet, incorrect resection with adverse outcomes. Given the lack of class 1 or 2 evidence on IEEG, a consensus-based expert recommendation on the diagnostic utility of IEEG is presented, with emphasis on the application of various modalities in specific substrates or locations, taking into account their relative efficacy, safety, ease, and incremental cost-benefit. These recommendations aim to curtail outlying indications that risk the over- or underutilization of IEEG, while retaining substantial flexibility in keeping with most standard practices at epilepsy centers and addressing some of the needs of resource-poor regions around the world.
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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 | 0.017 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".