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Record W4226425030 · doi:10.1055/s-0042-1746208

The Epileptology of Aicardi–Goutières Syndrome: Electroclinical Phenotype and Clinical–Radiological Correlation

2022· article· en· W4226425030 on OpenAlexaff
C. Varesio, Valentina De Giorgis, Maurizio Viri, Leone Giordano, Roberta La Piana, Davide Tonduti, Federico Roncarolo, Pierangelo Veggiotti, Elisa Maria Fazzi, Simona Orcesi

Bibliographic record

VenueNeuropediatrics · 2022
Typearticle
Languageen
FieldImmunology and Microbiology
Topicinterferon and immune responses
Canadian institutionsUniversité de MontréalMontreal Neurological Institute and Hospital
Fundersnot available
KeywordsMedicinePhenotypeEpilepsyNeuroscienceElectroencephalographyClinical phenotypePediatricsElectrophysiologyPsychiatryPsychologyGeneticsInternal medicineBiologyGene

Abstract

fetched live from OpenAlex

Objectives: Aicardi–Goutières syndrome (AGS) is a monogenic interferonopathy. Most individuals affected by AGS show various degrees of neurological symptoms. Although at least a quarter of patients with AGS experiences epileptic seizures, their phenotypic and electrophysiological characterizations remain elusive. To encompass this lack of knowledge, we collected patients with established diagnosis of typical AGS and detailed electroclinical data with the aim to characterize epilepsy phenotypes and electroencephalographic patterns and identify possible correlations between them and clinical, genetic, and neuroradiologic features. Content: Methods: This is a multicenter retrospective study. We enrolled 27 patients with established diagnosis of typical AGS, adequate EEG study, and contemporary neuroimaging. Medical records, EEGs, and MRI/CT findings were reviewed for each patient. In particular, 63 EEGs were independently and blindly reviewed by three board‐certified pediatric epileptologists. Results: Epilepsy is present in 37% (10 out of 27 patients), mean age at seizure onset was 9.5 (range: 1–36) months. Semiology was mainly represented by epileptic spasms and focal seizures. Significant correlation was found between presence of epilepsy and calcification severity ( p = 0.016) and presence of startle reactions ( p = 0.05). Concerning EEG pattern, background activity was disrupted in 85%. Organization was rated as disorganized or markedly disorganized in 73% of cases. Both background activity and EEG organization disruption appeared to be statistically related to high abnormality of MRI signal intensity ( p = 0.028 and 0.022). Disrupted EEG organization was also significantly related to microcephaly ( p < 0.001). Physiological organization was found to be more adequate in sleep (87%) than in wakefulness (38%). Focal slow activity was recorded in more than one third of cases. Fast activity was found more frequently in waking (78%), rather than in sleep (50%), either generalized or with frontal localization. Interictal epileptiform discharges (IEDs) were present in 33% of awake and in 45% of sleep recordings. Significant correlation was found between presence of IEDs during sleep and epileptic seizures ( p = 0.008). Conclusion: This is the first study providing detailed epileptological description and its correlation with clinical and neuroradiological data. Hallmarks of EEG pattern in our sample are represented by organization disruption, focal slow and fast activity, presence of IEDs both in patients with and without epilepsy. Epilepsy and EEG pattern are at least partly related to neuroradiologic findings, but their complex anatomo-electro-clinical basis still needs to be fully elucidated. Publication History Article published online: 16 March 2022 © 2022. Thieme. All rights reserved. Georg Thieme Verlag KG Rüdigerstraße 14, 70469 Stuttgart, Germany

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.014
GPT teacher head0.268
Teacher spread0.254 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2022
Admission routes1
Has abstractyes

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