Metabolic epilepsies: approaches to a diagnostic challenge.
Bibliographic record
Abstract
Although inborn errors of metabolism (IEM) are a relatively rare cause of epilepsy in children, their diagnosis is important with respect to treatment, prognosis and genetic counselling. In addition to seizures and epilepsy, IEM may produce a complex clinical picture in which epilepsy is only one of the various neurologic manifestations including developmental delay/regression, mental retardation, movement disorders, micro-/macrocephaly, as well as cerebral grey and white matter changes. Dysmorphic features and cerebral dysgenesis may also be part of a metabolic epilepsy syndrome (e.g. disorders of peroxisomal biogenesis, glutaric aciduria type 2, pyruvate dehydrogenease complex deficiency). Metabolic epilepsies may dominate the clinical presentation (e.g. pyridoxine dependent epilepsy) or may precede further neurologic deterioration (e.g. neuronal ceroid lipofuscinosis) and additional organ involvement (e.g. liver failure in Alpers (POLG1) disease). Metabolic epilepsies often present with myoclonic seizures (e.g. Gaucher Disease type 3, mitochondrial syndromes) and, as a rule, patients presenting with predominantly myoclonic seizures should be carefully investigated for these types of metabolic epilepsies. Patients with very early onset of epilepsy are considered at high risk for an underlying IEM as well. In this review we present an overview of metabolic epilepsies based on various criteria such as treatability, age of onset, seizure type, and pathogenetic background. Exemplary disorders will be described in more detail including cerebral glucose transporter (GLUT1) deficiency, pyridoxine dependent epilepsy, neuronal ceroid lipofuscinosis, cathepsin D deficiency, Alpers syndrome (POLG deficiency), and guanidinoacetate methyltransferase (GAMT) deficiency.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".