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Record W4387079742 · doi:10.1016/j.seizure.2023.09.022

Epidemiology of focal onset seizures in children aged >1 month to 4 years in Europe, United States, and Canada: A literature review

2023· review· en· W4387079742 on OpenAlexaboutno aff
Susanne Schubert‐Bast, Moninder Kaur, Lars Joeres, Nadia Foskett, Robert Roebling, Adam Strzelczyk

Bibliographic record

VenueSeizure · 2023
Typereview
Languageen
FieldMedicine
TopicEpilepsy research and treatment
Canadian institutionsnot available
FundersUCB Pharma GermanyUCB PharmaSchawarz Pharma
KeywordsEpidemiologyEpilepsyMedicinePediatricsIncidence (geometry)PopulationAge of onsetPsychiatryDiseaseInternal medicineEnvironmental health

Abstract

fetched live from OpenAlex

The present study aims to report the currently available epidemiology of focal onset seizures in children aged >1 month to 4 years with the help of a literature review. The terms 'seizure*' OR 'epilepsy' combined with pediatric and epidemiology terms were used to search Embase, PubMed, and Web of Science up to November 16, 2021. Due to the scarcity of epidemiology data on focal onset seizures, the incidence and prevalence were estimated using the proportion of focal onset seizures in epilepsy patients from the most recently published articles. The estimated annual incidence per 100,000 children of focal onset seizures in children of 0-4 years of age ranged from 25.1 (95 % confidence interval [CI] 18.9-32.7) in the United Kingdom to 111.8 in the United States. The estimated period prevalence of focal onset seizures in children 0-4 years of age ranged from 0.15 % (99 % CI 0.13-0.18) in Canada to 0.61 % in the United States. Neurodevelopmental outcomes and psychiatric disorders were the most commonly reported comorbidities in children with epilepsy of age 0-4 years. Presence of focal onset seizures in children with different epilepsy syndromes needs to be thoroughly considered in the treatment planning of this population of interest.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.303
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0030.000
Bibliometrics0.0010.003
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.045
GPT teacher head0.367
Teacher spread0.323 · 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 teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreReview

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
Published2023
Admission routes1
Has abstractyes

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