Culturally Informed Neuropsychological Evaluations in Pediatric Epilepsy: Evidence-Based Practice Considerations
Bibliographic record
Abstract
OBJECTIVE: Epilepsy is one of the most common reasons for referral for a pediatric neuropsychological evaluation due its high prevalence in childhood and our well-established clinical role in tertiary care settings. Emerging evidence indicates that racial and ethnic minority populations experience increased epilepsy burden compared with White peers. Although there has been heightened recognition in our specialty regarding the dire need for culturally and linguistically responsive evaluations, the scientific evidence to support effective neuropsychological service delivery for bi/multilingual and bi/multicultural youth with epilepsy is comparatively scant and of poor quality. As a result, significant patient and clinical challenges exist, particularly in high stakes presurgical pediatric epilepsy evaluations of bi/multilingual and bi/multicultural children. METHOD: Given that Spanish is the most common language spoken in the United States after English, this paper will focus on Spanish and English measures, but will provide evidence-based practice considerations that can inform practices with other non-English speaking communities. Cultural and linguistic factors that affect clinical decision-making regarding test selection, test interpretation, and feedback with families are highlighted. RESULTS: We offer a review of neuropsychological profiles associated with pediatric epilepsy as well as a flexible, multimodal approach for the assessment of linguistically and culturally diverse children with epilepsy based on empirical evidence and the clinical experiences of pediatric neuropsychologists from diverse backgrounds who work with children with epilepsy. CONCLUSION: Limitations to this approach are discussed, including the lack of available measures and resources for culturally and linguistically diverse pediatric populations. A case illustration highlights a culturally informed assessment approach.
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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 | 0.042 | 0.167 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".