Neuroimaging findings in children with COVID-19 infection: a systematic review and meta-analysis
Bibliographic record
Abstract
Abstract The COVID-19 pandemic has impacted individuals differently, and there's been a growing body of evidence pointing to neurological complications caused by the virus. However, our understanding of the range of neurological issues linked to SARS-CoV-2 infection in children is limited. This systematic review and meta-analysis aimed to assess the abnormal neuroimaging findings in pediatric COVID-19 patients, shedding light on this crucial aspect of the disease's impact on children. We conducted an extensive search in the PubMed, Medline, and ScienceDirect databases for observational studies reporting neuroimaging findings of the brain and spinal cord in children with COVID-19 between December 1, 2019, and October 30, 2021. Grey literature sources, including medRxiv and Google Scholar, were also explored. Pooled proportions of abnormal neuroimaging findings, categorized into neurovascular findings, ADEM-like lesions, encephalitic pattern, myelitis, transient splenial lesions, and other anomalies, were calculated using a random-effects model. Between-study heterogeneity was assessed using the χ2 statistic for pooled proportions and the inconsistency index I2. The Quality of the studies was evaluated using the NIH Quality Assessment Tool and the adapted Newcastle–Ottawa Scale. Our search yielded 9,605 articles, with 96 studies (involving 327 pediatric patients) included in the qualitative analysis. Of these, five reports (encompassing 111 patients) underwent quantitative analysis. The pooled proportion of pediatric COVID-19 patients with neurological symptoms and exhibiting abnormal neuroimaging findings was 43.74%. These findings were further categorized into neurovascular findings (8.22%), ADEM-like lesions (7.69%), encephalitic pattern (13.95%), myelitis (4.60%), transient splenial lesions (16.26%), and other abnormalities (12.03%). Insignificant between-study heterogeneity was observed in all categories, and our analysis did not reveal significant publication bias. In conclusion, a substantial proportion of pediatric COVID-19 patients with neurological symptoms have abnormal neuroimaging findings, underscoring the need for vigilant monitoring of neurological complications in this vulnerable population. Standardized reporting and long-term follow-up studies are essential to fully understand the implications of these findings. Collaborative research efforts will deepen our understanding of COVID-19's neurological dimensions in children and enhance clinical care for this population.
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 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 | 0.012 | 0.032 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.026 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".