Cortical Stimulation for brain mapping and seizure stimulation, pediatric practices throughout Canada: A national survey and systematic literature review
Bibliographic record
Abstract
INTRODUCTION: Diagnostic cortical stimulation (CS) in intracranial electroencephalography (iEEG) is an established epilepsy presurgical assessment tool to delineate relevant brain functions and elicit habitual epileptic seizures. Currently, no consensus exists as to whether CS should be routinely performed in pediatric patients. A significant challenge is their limited ability to cooperate during the procedure or to describe non-observable seizure semiology features. Our goal was to identify the spectrum of CS practices in Canada, for both eloquent cortex mapping and seizure stimulation. METHODS: An online survey, answered by all 8 Canadian pediatric epilepsy centers, enquired about implantation, stimulation methods, and use of standardized protocols. A systematic literature review extracted detailed stimulation parameters. RESULTS: Most of the institutions (n = 7/8) reported performing CS during presurgical evaluation. Four institutions indicated they perform stimulation in all implanted patients for the purpose of eloquent cortex mapping and seizure stimulation. The majority of physicians had their individual approach to CS. A largely variable approach to CS, mainly in the choice of stimulation parameters (i.e., train and pulse duration), was observed, with the highest variance concerning the purpose of seizure stimulation. The literature review highlighted an overall small sample size and minimal number of publications. Even though there is a rising trend towards stereotactic iEEG implantation, more data were available on subdural EEGs. CONCLUSION: This study shows individual and sparsely validated approach to CS in pediatric epilepsy. The literature review underscores the urgent need to harmonize pediatric intracranial EEG practices. More multicenter studies are needed to identify safe stimulation thresholds and allow implementation of evidence-based 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 | 0.005 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.018 | 0.032 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".