Inflammatory and coagulation marker profiles in severe pediatric COVID-19 patients: a systematic review
Bibliographic record
Abstract
Background Children are susceptible to SARS-CoV-2 infection and often present mild manifestations. However, severe and critical cases have also been reported. The inflammation and coagulation marker profile pattern in these patients along with the white blood cell differential count in critical PICU cases with non-COVID-19 etiology is not entirely clear. Objective To evaluate the inflammation and coagulation profiles in children presenting with severe/critical SARS-CoV-2 infection. Methods A systematic search and review of scientific literature was conducted following the PRISMA guidelines using ProQuest, SCOPUS, EBSCOHost, ScienceDirect, Cochrane, EMBASE, and Pubmed databases. All relevant original studies until March 11, 2021, were included. The risk of bias was appraised using the Modified Newcastle Ottawa Scale and JBI Critical Appraisal Checklist tools. Results We identified 14 studies across 6 countries, including a total sample of 159 severe and critically ill pediatric COVID-19 patients. Most of the subjects showed normal leukocytes, but increased CRP, procalcitonin, ferritin, and IL-6. Studies on coagulation profiles showed normal thrombocytes, PT, aPTT, and inconsistent D-dimer results. Conclusion Inflammation and coagulation parameters in severe/critically ill children with COVID-19 are atypical. Several inflammatory markers were elevated, including CRP, ferritin, procalcitonin, and IL-6. However, the elevated marker values are still lower compared to non-COVID infection patients. Further investigation of the parameters need to be done in serial examination multicenter studies, which include control subjects.
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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 | 0.005 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 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".