Epilepsy in Legius syndrome: Coincidence or causation?
Bibliographic record
Abstract
Legius syndrome is a rare genetic disorder, caused by heterozygous SPRED1 pathogenic variants, which shares phenotypic features with neurofibromatosis type 1 (NF1). Both conditions typically involve café-au-lait macules, axillary freckling, and macrocephaly; however, patients with NF1 are also at risk for tumors, such as optic nerve gliomas and neurofibromas. Seizure risk is known to be elevated in NF1, but there has been little study of this aspect of Legius syndrome. The reported epilepsy incidence is 3.3%-5%, well above the general population incidence of ~0.5%-1%, but the few reports in the literature have very little data regarding epilepsy phenotype. We identified two unrelated individuals, both with Legius syndrome and epilepsy, and performed thorough phenotyping. One individual's mother also had Legius syndrome and now-resolved childhood epilepsy, as well as reports of more distant relatives who also had multiple café-au-lait macules and seizures. Both probands had experienced childhood-onset focal seizures, with normal brain MRI. In one patient, EEG later showed apparently generalized epileptiform abnormalities. Based on the data from this small case series and literature review, seizure risk is increased in people with Legius syndrome, but the epilepsy prognosis appears to be generally good, with patients having either self-limited or pharmacoresponsive courses.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".