COVID-19 in Children with Brain-Based Developmental Disabilities: A Rapid Review
Bibliographic record
Abstract
Abstract Background The prevalence of symptomatic COVID-19 in children remains low to date. In just a few months, COVID-19 has affected millions of people worldwide, and as of the date of this publication, the pandemic continues. Based on the current available evidence, children do not appear to be at higher risk of contracting COVID-19 than adults. However, children with neurological and neuromuscular conditions are vulnerable to the respiratory complications of other viral infections. Objectives To assess whether children with brain-based developmental disabilities were more likely to develop COVID-19 and have complications or poorer outcomes following infection. Methods We conducted a two-week rapid review on studies with primary data regarding children aged between zero and 18 years old with brain-based developmental disabilities, or who were at risk of developing such disabilities, with confirmed or suspected COVID-19. We performed our literature searches on April 18, 2020. Results Our search strategy identified 538 individual records, of which four were included in our review. Of the 50 COVID-19 pediatric patients reported in the included studies, a total of seven children were at risk of developing brain-based disabilities. Symptoms ranged in severity. However, generally, patients were discharged or saw improvements in their symptoms by the end of the study period. No deaths were reported. Discussion Our study highlights a knowledge gap regarding the impact of COVID-19 in children with brain-based developmental disabilities.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.013 | 0.011 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".