Bibliographic record
Abstract
Epidermal nevus syndrome (ENS) encompasses a group of congenital genetic disorders characterized by the association of an epidermal nevus with systemic anomalies including the brain. The most frequent subtype with neurological features is linear sebaceous nevus syndrome (LSNS), followed by keratinocytic nevus syndrome (ENS). Both are caused by postzygotic RAS mutations. All forms of ENS are mosaic disorders that share a common embryological basis and pathogenesis as neurocristopathies . The lines of Blaschko indicate the cutaneous migratory pathways of neural crest. Neural crest defects explain the development of nevi and associated multisystemic anomalies: brain, ocular, osseous, muscular, orodental, cardiovascular, endocrine, renal/urinary, and adipose tissue. Epilepsy and cognitive and motor deficits are caused mainly by hemimegalencephaly (HME), a closely related mosaic anomaly caused by somatic mutations in PI3K–AKT3–mTOR pathway that determines overgrowth. The usual onset of epilepsy is in the neonatal period or early infancy, often as infantile spasms. The pioneer description of nevus sebaceous in 1895 by Jadassohn is the basis for delineating the LSNS. The eponym “Schimmelpenning syndrome” as a substitute for that long-established term makes no sense in the context of the large number of authors who contributed to defining this syndrome. A review of 111 cases of LSNS and other subtypes enabled a classification of neurological phenotypes; epilepsy occurred in 77% and HME in 38%. Three distinctive additional syndromes also are within the spectrum of ENS: CLOVES, SCALP, and Heide's syndrome. The impact of epilepsy in patients affected by ENS is related to time of onset and extent of brain anomalies. Timing of the mutation determines the phenotype and severity of the systemic lesions.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".