Preventable and Unpreventable Causes of Childhood-Onset Epilepsy Plus Mental Retardation
Bibliographic record
Abstract
OBJECTIVE: The objective of this study was to determine the causes of childhood epilepsy associated with mental retardation and determine whether these causes are preventable. METHODS: We selected all patients from the Nova Scotia population-based childhood epilepsy cohort (n = 692) who had mental retardation and had epilepsy onset between 1977 and 1985. Causes and family history were determined by chart review and caregiver interview after 18.8 (SD: +/-7) years of follow-up. RESULTS: Overall, 147 patients had mental retardation and epilepsy (21% of all childhood epilepsy). Standard psychological testing was available for 57%; 38.5% were too impaired for testing, which left 4% with the degree of mental retardation assessed clinically. Severe/profound mental retardation predominated (mild: 24%; moderate: 23%, severe/profound: 53%). Fifty-nine percent had additional severe neurologic deficits, most often associated with severe mental retardation. Epilepsy syndromes were symptomatic generalized (n = 73), partial (n = 58), and other (n = 16). Most had a brain imaging study: 91% had a computed tomography scan, and 12% had an MRI scan. Sixty-three percent had a defined cause; 37% had an unknown cause. A defined cause was more likely in those with severe mental retardation (60 of 78 vs 31 of 65). Identified causes were prenatal or genetic (65%), perinatal (8%), or complications of prematurity (13%). Only 11 (7%) had an acquired cause that was potentially preventable. Many (36%) had a first- or second-degree relative with epilepsy, more often in those without a clear cause (54% vs 30%) and without additional neurologic disability (57% vs 26%). CONCLUSIONS: Approximately 20% of children with epilepsy have mental retardation. The cause is prenatal or genetic in nearly two thirds, and only 7% have an acquired, preventable cause. Important genetic influences may be present, especially in the absence of a defined cause.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.003 | 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".