ILAE survey of neuropsychology practice in pediatric epilepsy surgery evaluation
Bibliographic record
Abstract
To determine the extent to which specific neuropsychological measures are in common use around the world for the assessment of children who are candidates for epilepsy surgery. As part of the work of the International League Against Epilepsy Pediatric Surgical Task Force, a survey was developed and distributed online. The survey consisted of questions related to demographics, training experience, general practice, and specific measures used and at what frequency. Seventy-eight clinicians with an average of 13.5 years of experience from 19 countries responded to the survey; 69% were English-speaking. Pre- and post-neuropsychological evaluations were conducted with a majority of children undergoing surgical resection for epilepsy. There was high consistency (>90%) among the domains evaluated, while consistency rate among specific measures was more variable (range: 0-100%). Consistency rates were also lower among respondents in non-English-speaking countries. For English-speaking respondents, at least one measure within each domain was used by a majority (>75%) of clinicians; 19 specific measures met this criterion. There is consensus of measures used in neuropsychological studies of pediatric epilepsy patients which provides a basis for determining which measures to include in establishing a collaborative data repository to study surgical outcomes of pediatric epilepsy. Challenges include selecting measures that promote collaboration with centers in non-English-speaking countries and providing data from children under age 5.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 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) | 0.003 | 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".