Bibliographic record
Abstract
Although Rolandic epilepsy (RE) is considered to be unrelated to severe encephalopathy, there is evidence about the disturbance of certain cognitive functions, especially speech and language. However, opinion regarding the relationship of cognitive decline and age at seizure onset is conflicting. Aim of the study: To assess the relationship of language function and age at seizure onset in children with RE. Methods: We studied language abilities (verbal fluency, speeded naming and comprehension of instructions) by using NEPSY test (A Developmental Neuropsychological Assessment) in 61 patients (pts) with RE aged 5 to 14 years admitted toKaunasUniversityHospital as inpatients.Age at seizure onset was considered the age of the patient at the first unprovoked seizure. Two-step Cluster Analysis and Schwarz’s Bayesian Criterion, ANOVA, Kruskal–Wallis Test andDunn’s post hoc test were used for statistical analysis. Results: Mean age at seizure onset was 7.8±2.4 years, divided into three age clusters (groups): <6 years–13pts (I), 6 to 9 years–29 pts (II), and >9 years–19 pts (III). Mean results of the verbal fluency task were 21.2 (SD13.3) in group I, 37.2 (SD12.1) in group II, 48.7 (SD9.3) in group III (p<0.01). Just the number of errors in speeded naming task did not differ within the groups, while the time of speeded naming task accomplishment and the comprehension of instructions (part I and II) showed correlation with age at onset, early age being determinant of less favorable results (p<0.05). Conclusion: Language difficulties in children with RE showed relationship to early age of seizure onset.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.790 | 0.567 |
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".