Supplementing Extraoperative Electrocorticography With Real-Time Intraoperative Recordings Using the Same Chronically Implanted Electrodes
Bibliographic record
Abstract
BACKGROUND: The practice of intraoperative electrocorticography (iECoG) to guide resective epilepsy surgery is variable. Limitations of iECoG include variability in recordings from previously unsampled cortex, increased operative time and cost, and a lack of clear benefit to surgical decision-making. OBJECTIVE: To describe a simple technique to supplement extraoperative intracranial recordings with real-time iECoG using the same chronically implanted electrodes that overcome some of these limitations. METHODS: We describe the technical procedure, intraoperative findings, and outcomes of 7 consecutive children undergoing 2-stage resective epilepsy surgery with invasive subdural grid monitoring between January 2017 and December 2019. All children underwent placement of subdural grids, strips, and depth electrodes. Planned neocortical resection was based on extraoperative mapping of ictal and interictal recordings. During resection in the second stage, the same electrodes were used to perform real-time iECoG. RESULTS: Real-time iECoG using this technique leads to modification of resection for 2 of the 7 children. The first was extended due to an electroencephalographic seizure from a distant electrode not part of the original resection plan. The second was restricted due to attenuation of epileptiform activity following a partial resection, thereby limiting the extent of a Rolandic resection. No infections or other adverse events were encountered. CONCLUSION: We report a simple technique to leverage chronically implanted electrodes for real-time iECoG during 2-stage resective surgery. This technique presents fewer limitations than traditional approaches and may alter intraoperative decision-making.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".