Seizure in Neurodegeneration with Brain Iron Accumulation: A Systematic Review
Bibliographic record
Abstract
BACKGROUND: Neurodegeneration with brain iron accumulation (NBIA) is a rare genetic disorder. Its clinical manifestations comprise a wide spectrum mainly movement disorders. Seizure as a clinical manifestation is known to occur in some NBIAs, but the exact prevalence of epilepsy in each individual disorder is not well elucidated. The aim of this review was to investigate the frequency of seizures in NBIA disorders as well as to determine the associated features of patients with seizures. METHOD: The electronic bibliographic databases PubMed, Scopus, Embase, and Google Scholar were systematically searched for all cases in any type of article from inception to December 16, 2019. All the reported cases of NBIA (with or without genetic confirmation) were identified. Case reports with an explicit diagnosis of any types of NBIA, which have reported occurrence (or absence) of any type of seizure or epilepsy, in the English language, were included. Seizure incidence rate, type, and age of onset were reported as frequencies and percentages. RESULT: 1698 articles were identified and 51 were included in this review. Of 305 reported cases, 150 (49.2%) had seizures (phospholipase A2-associated neurodegeneration (PLAN) = 64 (50.8%), beta-propeller protein-associated neurodegeneration (BPAN) = 57 (72.1%), pantothenate kinase-associated neurodegeneration (PKAN) = 11 (23.4%), and others = 18 (very variable proportions)). The most frequent seizure type in NBIA patients was generalized tonic-clonic seizure with the mean age of seizure onset between 2 and 36 years. However, most of these papers had been published before the new classification of epilepsy became accessible. Affected patients were more likely to be females. CONCLUSION: Seizures are common in NBIA, particularly in PLAN and BPAN. In PKAN, the most common type of NBIA, around 10% of patients are affected by seizures. BPAN is the most possible NBIA accompanying seizure. Most of the findings regarding the seizure characteristics in the NBIAs are biased due to the huge missing data. Therefore, any conclusions should be made with caution and need further investigations.
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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.012 | 0.014 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".