Establishing criteria for pediatric epilepsy surgery center levels of care: Report from the ILAE Pediatric Epilepsy Surgery Task Force
Bibliographic record
Abstract
Presurgical evaluation and surgery in the pediatric age group are unique in challenges related to caring for the very young, range of etiologies, choice of appropriate investigations, and surgical procedures. Accepted standards that define the criteria for levels of presurgical evaluation and epilepsy surgery care do not exist. Through a modified Delphi process involving 61 centers with experience in pediatric epilepsy surgery across 20 countries, including low-middle- to high-income countries, we established consensus for two levels of care. Levels were based on age, etiology, complexity of presurgical evaluation, and surgical procedure. Competencies were assigned to the levels of care relating to personnel, technology, and facilities. Criteria were established when consensus was reached (≥75% agreement). Level 1 care consists of children age 9 years and older, with discrete lesions including hippocampal sclerosis, undergoing lobectomy or lesionectomy, preferably on the cerebral convexity and not close to eloquent cortex, by a team including a pediatric epileptologist, pediatric neurosurgeon, and pediatric neuroradiologist with access to video-electroencephalography and 1.5-T magnetic resonance imaging (MRI). Level 2 care, also encompassing Level 1 care, occurs across the age span and range of etiologies (including tuberous sclerosis complex, Sturge-Weber syndrome, hypothalamic hamartoma) associated with MRI lesions that may be ill-defined, multilobar, hemispheric, or multifocal, and includes children with normal MRI or foci in/abutting eloquent cortex. Available Level 2 technologies includes 3-T MRI, other advanced magnetic resonance technology including functional MRI and diffusion tensor imaging (tractography), positron emission tomography and/or single photon emission computed tomography, source localization with electroencephalography or magnetoencephalography, and the ability to perform intra- or extraoperative invasive monitoring and functional mapping, by a large multidisciplinary team with pediatric expertise in epilepsy, neurophysiology, neuroradiology, epilepsy neurosurgery, neuropsychology, anesthesia, neurocritical care, psychiatry, and nursing. Levels of care will improve safety and outcomes for pediatric epilepsy surgery and provide standards for personnel and technology to achieve these levels.
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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 | 0.084 | 0.124 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".