Neuropsychological Outcome in Perinatal Stroke Associated With Epileptiform Discharges in Sleep
Bibliographic record
Abstract
BACKGROUND: Patients with arterial perinatal stroke often suffer long-term motor sequelae, difficulties in language, social development, and behaviour as well as epilepsy. Despite homogeneous lesions, long-term behavioural and cognitive outcomes are variable and unpredictable. Sleep-related epileptic encephalopathies can occur after early brain injury and are associated with global developmental delays. We hypothesized that sleep-potentiated epileptiform abnormalities are associated with worse developmental outcomes after perinatal stroke. METHODS: Participants were identified from a population-based cohort (Alberta Perinatal Stroke Project). Inclusion criteria were magnetic resonance imaging-confirmed arterial perinatal stroke, age 4 to 18 years, electroencephalogram (EEG) including sleep, and comprehensive neuropsychological evaluation. Sleep-related EEG abnormalities were categorized by an epileptologist blinded to the cognitive outcome. Associations between EEG classification and neuropsychological outcomes were explored (t tests, Bonferroni correction for multiple comparisons). RESULTS: Of 128 potentially eligible participants, 34 (53% female) had complete EEG (mean age, 8.1 years; range, 0.2-16.4) and neuropsychology testing (mean age, 9.8 years; range 4.4-16.7). Twelve (35%) were classified as having electrical status epilepticus in sleep. Patients with abnormal EEGs were more likely to have statistically worse scores when corrected for multiple comparisons, in receptive language (median, 1st percentile; IQR 1-7th percentile; p<0.05), and externalizing behaviours (median, 82nd percentile; IQR, 79-97th percentile; p<0.05). CONCLUSIONS: Developmental outcome in language and behaviour in children with arterial perinatal stroke is associated with electrical status epilepticus in sleep. Increased screening with sleep EEG is suggested, whereas further studies are necessary to determine if treatment of EEG abnormalities can improve outcome.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".