Clinical implications of naming performance and seizure lateralization in bilingual children with epilepsy
Bibliographic record
Abstract
OBJECTIVE: Naming difficulty is a common symptom of left (i.e., language dominant) hemisphere epilepsy. As such, in the presurgical evaluation for drug-resistant epilepsy, which aims to localize the epileptogenic region, identification of a naming deficit typically implicates the left temporal region. However, the well-established finding of poor naming in those with left but not right (i.e., nondominant) hemisphere seizures in monolingual patients is unreliable in bilingual adults with epilepsy, despite proficiency in the language tested. We aimed to examine naming performance and its relation with seizure lateralization in bilingual children with epilepsy. METHODS: This multisite study included 57 bilingual and 202 monolingual pediatric epilepsy patients, aged 6-17 years. All patients underwent neuropsychological evaluation including assessment of auditory and visual object naming in English. RESULTS: In the context of age-appropriate English expressive vocabulary skills, bilingual children with epilepsy demonstrated significantly weaker auditory and visual naming than monolingual patients. Additionally, unlike monolingual patients, who showed poorer naming among those with left compared to those with right hemisphere seizures, bilingual children with unilateral left and right hemisphere seizures demonstrated similarly weak naming performances. Furthermore, naming score cutoffs failed to differentiate individual bilingual patients with left versus right hemisphere seizure onset as they did among monolingual patients. SIGNIFICANCE: Despite conversational proficiency and normal English expressive vocabulary, the relation between seizure laterality and naming performance demonstrated in monolingual children with unilateral seizures was not observed in a comparable group of bilingual children. Consequently, poor naming performance in bilingual children with epilepsy may be misinterpreted, most seriously in those with nondominant hemisphere seizures, as scores may be erroneously interpreted to reflect dominant hemisphere seizure involvement, potentially leading to unnecessary invasive and costly procedures. Results suggest cautious interpretation of naming performance in bilingual children with epilepsy.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".