The Spectrum and Burden of COVID-19–Associated Neurologic Disease in Australian Children 2020–2023
Bibliographic record
Abstract
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.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".