A Systematic Review of Persistent Clinical Features After SARS-CoV-2 in the Pediatric Population
Bibliographic record
Abstract
CONTEXT: Long-term health effects after coronavirus disease 2019 (COVID-19) have been increasingly reported but their prevalence and significance in the pediatric population remains uncertain. OBJECTIVE: To present the prevalence and characteristics of the long-term clinical features of COVID-19 (long COVID) in the global pediatric population. DATA SOURCES: PubMed, Embase, Web of Science, Cochrane Library, WHO COVID-19 database, google scholar, medRxiv, bioRxiv, and multiple national public health databases. STUDY SELECTION: Published articles and preprints from December, 2019 to December, 2022 investigating the epidemiology and characteristics of persistent clinical features at least 3 months after COVID-19 in children and adolescents (0-19 years old) were included. DATA EXTRACTION: Study characteristics and detailed description of long COVID were extracted into a predefined form. RESULTS: Twenty seven cohorts and 4 cross-sectional studies met the inclusion criteria and involved over 15 000 pediatric participants. A total of more than 20 persistent symptoms and clinical features were reported among children and adolescents. 16.2% (95% confidence interval 8.5% to 28.6%) of the pediatric participants experienced 1 or more persistent symptom(s) at least 3 months post COVID-19. Female gender might be associated with developing certain long COVID symptoms. LIMITATIONS: Included studies presented with great heterogeneity because of significant variations in the definition of "long COVID," follow up duration, and method. There could be nonresponse and other potential bias. CONCLUSIONS: Persistent clinical features beyond 3 months among children and adolescents with proven COVID-19 are common and the symptom spectrum is wide. High-quality, prospective studies with proper controls are necessary in the future.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".