Neurological involvement in hospitalized children with SARS-CoV-2 infection: a multinational study
Bibliographic record
Abstract
ABSTRACT: Background and Objectives: Neurological involvement associated with SARS-CoV-2 infection is increasingly recognized. However, the specific characteristics and prevalence in pediatric patients remain unclear. The objective of this study was to describe the neurological involvement in a multinational cohort of hospitalized pediatric patients with SARS-CoV-2. Methods: This was a multicenter observational study of children <18 years of age with confirmed SARS-CoV-2 infection or multisystemic inflammatory syndrome (MIS-C) and laboratory evidence of SARS-CoV-2 infection in children, admitted to 15 tertiary hospitals/healthcare centers in Canada, Costa Rica, and Iran February 2020–May 2021. Descriptive statistical analyses were performed and logistic regression was used to identify factors associated with neurological involvement. Results: One-hundred forty-seven (21%) of 697 hospitalized children with SARS-CoV-2 infection had neurological signs/symptoms. Headache ( n = 103), encephalopathy ( n = 28), and seizures ( n = 30) were the most reported. Neurological signs/symptoms were significantly associated with ICU admission (OR: 1.71, 95% CI: 1.15–2.55; p = 0.008), satisfaction of MIS-C criteria (OR: 3.71, 95% CI: 2.46–5.59; p < 0.001), fever during hospitalization (OR: 2.15, 95% CI: 1.46–3.15; p < 0.001), and gastrointestinal involvement (OR: 2.31, 95% CI: 1.58–3.40; p < 0.001). Non-headache neurological manifestations were significantly associated with ICU admission (OR: 1.92, 95% CI: 1.08–3.42; p = 0.026), underlying neurological disorders (OR: 2.98, 95% CI: 1.49–5.97, p = 0.002), and a history of fever prior to hospital admission (OR: 2.76, 95% CI: 1.58–4.82; p < 0.001). Discussion: In this study, approximately 21% of hospitalized children with SARS-CoV-2 infection had neurological signs/symptoms. Future studies should focus on pathogenesis and long-term outcomes in these children.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".