COVID-19 in Children with Brain-Based Developmental Disabilities: A Rapid Review Update
Bibliographic record
Abstract
Abstract Objective Information regarding the impact of COVID-19 in children with brain-based disabilities, or those at risk of developing such conditions, remains scarce. The objective was to evaluate if children with brain-based disabilities are more likely to (1) develop COVID-19, (2) develop complications from the disease, and (3) to have a poorer prognosis. Study design We conducted a rapid review using search strategies iteratively developed and tested by an experienced medical information specialist in consultation with the review team and a panel of knowledge users. Searches were initially performed on April 18th, 2021, and updated on October 31st, 2020. Four reviewers individually performed study selection using pilot-tested standardized forms. Single reviewers extracted the data using a standardized extraction form that included study characteristics, patients’ characteristics, and outcomes reported. Results We identified 1448 publications, of which 29 were included. Studies reported data on 2288 COVID-19 positive children, including 462 with a brain-based disability, and 72 at risk of developing such disability. Overall, the included studies showed a greater risk to develop severe COVID-19 disease in children with brain-based disabilities. Although mortality is very low, the case-fatality rate appeared to be higher in children with disabilities compared to children without disabilities. Conclusions Our review shows that children with brain-based disabilities are overrepresented in hospitalization numbers compared to children without disabilities. However, most studies included children that were hospitalized from COVID-19 in secondary and tertiary care centers. Results of this review should therefore be interpreted with caution.
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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 | 0.010 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.018 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".