P.094 Referral Practices for Epilepsy Surgery in Pediatric Patients: A North American Study
Bibliographic record
Abstract
Background: The International League Against Epilepsy recommends patients with drug resistant epilepsy (DRE) be referred for surgical evaluation, however prior literature suggests this is an underutilized intervention. This study captures practices of North American pediatric neurologists regarding the management of DRE and factors which may promote or limit referrals for epilepsy surgical evaluation. Methods: A REDCap survey distributed via the Child Neurology Society mailing list to pediatric neurologists practicing in North America. “R” was used to conduct data analyses. Ethics approval from the CHEO REB was granted prior to the start of data collection. Results: 102 pediatric neurologists responded, 77% of whom currently practice in the United States. 73% of respondents reported they would refer a patient for surgical consultation after two failed medications. Of all potential predictors tested in a logistic regression model, low referral volume was the only predictor of whether participants refer patients after more than three failed medications. Conclusions: Pediatric neurologists demonstrate fair knowledge of formal recommendations to refer patients for surgical evaluation after two failed medication trials. 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 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".