Seizures and electroencephalographic findings in inborn errors of metabolism: Clues to differential diagnosis in the neonatal period, infancy, childhood and adolescence, and review of the literature
Bibliographic record
Abstract
Although inborn errors of metabolism (IEM) are a rare cause of epilepsy, seizures are a common presentation in these disorders. Seizures in IEM are frequently refractory to conventional anti-seizure medication and might warrant initiation of specific treatments based on vitamins or dietary modifications or provision of alternative substrates (to bypass a block). In most IEMs, seizures present with variable and non-specific semiology and EEG findings. Nevertheless, certain distinctive electro-clinical features might suggest an underlying IEM and even point towards the etiology. Even though biochemical and genetic tests will confirm the definitive diagnosis, EEG has the great advantage of being rapidly available, before the results of these tests. This article aims to describe through a literature review, illustrated by videos and EEGs from personal cases, seizures, and EEG patterns in IEM. We will focus on distinctive electro-clinical features that may help the clinician to establish an appropriate diagnosis allowing a prompt and specific treatment in order to improve outcome.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".