Referral practices for epilepsy surgery in pediatric patients: A North American Study
Bibliographic record
Abstract
OBJECTIVE: Patients with epilepsy who do not respond to two trials of appropriate antiepileptic drugs are considered to have drug-resistant epilepsy (DRE). The International League Against Epilepsy recommends patients with DRE be referred for surgical evaluation; however, prior literature suggests this is an underutilized intervention, especially in the pediatric setting. This study captures practices of North American pediatric neurologists regarding the management of DRE and factors that may promote or limit referrals for epilepsy surgical evaluation. METHODS: A REDCap survey was distributed via the Child Neurology Society mailing list to pediatric neurologists practicing in North America. Ethics approval from the Children's Hospital of Eastern Ontario Research Ethics Board was granted prior to the start of data collection. RESULTS: Ninety-eight responses were included in the analysis; 77% of participants currently practice in the United States; 73.5% of respondents reported they would refer a patient for surgical consultation after two failed medications. Of all potential predictors tested in a binary logistic regression model, only annual referral volume predicted whether participants refer patients after three or more failed medications. Centers with high referral volume were 37% more likely to adhere to the guideline of referral after two failed medications. SIGNIFICANCE: Pediatric neurologists demonstrate fair knowledge of formal recommendations to refer patients for surgical evaluation after two failed medication trials, although referral rates remain unacceptably low. Participants continue to report that they would not refer patients with generalized electroencephalographic findings for surgical evaluation; this should continue to be addressed. Other modifiable factors reported, especially family perceptions of epilepsy surgery, should be prioritized when developing tools to enhance effective referrals and increase utilization of epilepsy surgery in the management of pediatric DRE.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".