Diagnostic utility of invasive<scp>EEG</scp>for epilepsy surgery: Indications, modalities, and techniques
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 it