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Record W4409451348 · doi:10.1097/inf.0000000000004830

The Spectrum and Burden of COVID-19–Associated Neurologic Disease in Australian Children 2020–2023

2025· article· en· W4409451348 on OpenAlexaff
Kara DuBray, Katherine Phan, Andrew Anglemyer, Rebecca Burell, Christopher C. Blyth, Jeremy Carr, Julia Clark, Nigel W. Crawford, Joshua Francis, Helen Marshall, Brendan McMullan, Michaela Waak, Russell C. Dale, Cheryl Jones, Emma Carey, Kristine Macartney, Nicholas Wood, Philip N Britton

Bibliographic record

VenueThe Pediatric Infectious Disease Journal · 2025
Typearticle
Languageen
FieldMedicine
TopicLong-Term Effects of COVID-19
Canadian institutionsKensington Health
Fundersnot available
KeywordsMedicinePediatricsEncephalitisIncidence (geometry)EncephalopathyCoronavirus disease 2019 (COVID-19)DiseaseAcute disseminated encephalomyelitisInternal medicineInfectious disease (medical specialty)Virology

Abstract

fetched live from OpenAlex

BACKGROUND: We aimed to describe the clinical spectrum and burden of COVID-19-associated neurologic disease in Australian children. METHODS: We extracted Australian national sentinel site surveillance data on COVID-19-associated neurologic disease in children hospitalized in the Paediatric Active Enhanced Disease Surveillance network, 2020-2023. Neurologic complications included encephalitis, encephalopathy, Guillain-Barre syndrome, seizures and cerebrovascular accident among others. We calculated the proportion of hospitalized pediatric COVID-19 cases associated with neurologic disease and described the spectrum of presentations including clinical features and severity. We calculated incidence rates of neurologic disease within COVID-19 variant eras among hospitalized patients. RESULTS: We identified 311 cases of SARS-CoV-2 infection with neurologic disease among 4616 hospitalized pediatric cases of COVID-19 reported through the surveillance network, representing 5.3 cases per 100 pediatric COVID-19 admissions. The most common COVID-19-associated neurologic presentations were seizures (n = 215), including febrile seizures. Nonspecific encephalopathy (n = 62), encephalitis, Guillain-Barre Syndrome, acute cerebellar syndromes, acute demyelinating encephalomyelitis and cerebrovascular accident were also reported. Almost 60% of children were ≤4 years, approximately 30% had pre-existing neurologic conditions and almost half had other medical comorbidities. COVID-19-associated neurologic complications infrequently led to death, although 25% (n = 2/8) of children with COVID-19 encephalitis died. The incidence rate of COVID-19-associated neurologic disease was lowest during the late Omicron era. CONCLUSIONS: Neurologic complications among COVID-19 hospitalized children are relatively frequent. While most neurologic complications are transient, including seizures, encephalitis remains a cause of significant morbidity. Children with pre-existing neurologic disease and other comorbidities are at higher risk.

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)0.001
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 score0.126
Threshold uncertainty score0.250

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.006
GPT teacher head0.277
Teacher spread0.271 · 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
Published2025
Admission routes1
Has abstractyes

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