Risk Factors for COVID-19 Mortality in Pediatric Populations: A Scoping Review
Bibliographic record
Abstract
Abstract Background Pediatric populations are generally considered to be at a lower risk of mortality from COVID-19 infection in comparison to adult populations. Regardless, a significant number of deaths from COVID-19 have been reported in pediatric populations internationally. We conducted a scoping review of the literature assessing the risk factors for COVID-19 mortality among pediatric patients. Methods A scoping review was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR). Searches were performed in PubMed, Scopus, medRxiv, and WHO Coronavirus Database. There were no restrictions placed for searches based on date. Papers that were published in English, included at least one pediatric death from COVID-19, and described at least one risk factor for the death and/or clinical presentation of the child(ren) were eligible for inclusion. The pediatric population was defined as children aged 18 years and younger. Results Searches generated a total of 5787 papers and, 78 papers were eligible for analysis. There was a pooled total of 837 pediatric deaths. The presence of at least one comorbidity was a major risk factor; malignancies, cardiovascular diseases and overweight/obesity were the most frequently associated with mortality. The development of Pediatric Multisystem Inflammatory Syndrome (PMIS) was also consistently demonstrated to be a risk factor. Common clinical complications associated with pediatric COVID-19 infection resulting in mortality were sepsis, acute respiratory distress syndrome, and acute kidney injury. Conclusions Our review has identified prominent risk factors for mortality from COVID-19 infection amongst pediatric patients. Knowledge of these risk factors can assist prognostication and clinical decision-making for severe pediatric COVID-19 infections. Our findings will help shape public health guidelines and guide new treatment regimens for high-risk individuals.
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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 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.008 |
| Bibliometrics | 0.015 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".