Long‐term outcomes of children with drug‐resistant epilepsy across multiple cognitive domains
Bibliographic record
Abstract
Aim To simultaneously evaluate long‐term outcomes of children with drug‐resistant epilepsy (DRE) across multiple cognitive domains and compare the characteristics of participants sharing a similar cognitive profile. Method Participants were adolescents and young adults (AYAs) diagnosed with DRE in childhood, who completed a comprehensive neuropsychological battery evaluating intelligence, memory, academic, and language skills at the time of surgical candidacy evaluation and at long‐term follow‐up (4–11y later). Hierarchical k‐means clustering identified subgroups of AYAs showing a unique pattern of cognitive functioning in the long‐term. Results Participants (n=93; mean age 20y 1mo [standard deviation {SD} 4y 6mo]; 36% male) were followed for 7 years (SD 2y 4mo), of whom 65% had undergone resective epilepsy surgery. Two subgroups with unique patterns of cognitive functioning were identified, which could be broadly categorized as ‘impaired cognition’ (45% of the sample) and ‘average cognition’ (55% of the sample); the mean z‐score across cognitive measures at follow‐up was −1.86 (SD 0.62) and −0.23 (SD 0.54) respectively. Surgical and non‐surgical patients were similar with respect to seizure control and their long‐term cognitive profile. AYAs in the average cognition cluster were more likely to have better cognition at baseline, an older age at epilepsy onset, and better seizure control at follow‐up. Interpretation The underlying abnormal neural substrate and seizure control were largely associated with long‐term outcomes across cognitive domains. What this paper adds Subgroups of adolescents and young adults that shared a similar pattern of cognitive functioning were identified. Long‐term cognitive profiles were similar for surgical and non‐surgical patients. Cognitive profiles were associated with baseline cognition, age at epilepsy onset, and seizure control.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".