The diagnostic utility of intracranial <scp>EEG</scp> monitoring for epilepsy surgery in children
Bibliographic record
Abstract
OBJECTIVE: There are limited data on the indications for the use of chronic invasive electroencephalography (EEG) monitoring (IEM) for pediatric epilepsy surgery. METHODS: We retrospectively studied 102 children who underwent intracranial monitoring to map critical cortex, localize the epileptogenic region, or resolve divergent findings. We assessed IEM utility based on changes to the resection plan following analysis of noninvasive data. RESULTS: IEM was judged useful in 87% of cases and had greatest utility for resolving discordant data and localizing extratemporal and multilobar epileptogenic zones. IEM data were least useful for seizure onset in the temporal lobe and had little utility for direct cortical stimulation mapping unless functional magnetic resonance imaging (fMRI) revealed atypical language representation or the epileptogenic zone was in proximity to critical cortex. SIGNIFICANCE: IEM utility was demonstrated for a majority of cases with well-defined indications. The method of assessing utility will facilitate multicentric studies toward developing future consensus and practice guidelines.
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".