Hematologic manifestations of SARS-CoV-2 infection and MIS-C in hospitalized children. Results of the PICNIC registry.
Bibliographic record
Abstract
Introduction: Hematologic complications of SARS-CoV-2 infection are well described in hospitalized adults with correlation to adverse outcomes. Information published in children has been limited. Methods: An international multi-centered retrospective registry was established to collect data on the clinical manifestations of SARS-CoV-2 or multisystem inflammatory syndrome (MIS-C) in hospitalized children between February 1, 2020 – May 31, 2021. This sub-study focused on hematologic manifestations. Study variables included patient demographics, comorbidities, clinical presentation, course, laboratory parameters, management, and outcomes. Results: Nine hundred and eighty-five children were enrolled and 915 (93%) had clinical information available; 385 (42%) had symptomatic SARS-CoV-2 infection upon admission, 288 had MIS-C (31.4%) and 242 (26.4%) had alternate diagnosis with SARS-CoV-2 identified incidentally. During hospitalization, 10 children (1%) experienced a thrombotic event, 16 (1.7%) had hemorrhage and 2 (0.2%) had both thrombotic and hemorrhagic episodes. Significant prothrombotic comorbidities included congenital heart disease (p-value = 0.007), central venous catheter (p = 0.04) in children with primary SARS-CoV-2 infection; and obesity (p-value= 0.002), cytokine storm (p= 0.012) in those with MIS-C. Significant pro- hemorrhagic conditions included age > 10 years (p = 0.04), CVC (p= 0.03) in children with primary SARS-CoV-2infection; and thrombocytopenia (0.001), cytokine storm (0.02) in those with MIS-C. Eleven patients died (1.2 %) with no deaths attributed to thrombosis or hemorrhage Conclusion: Thrombotic and hemorrhagic complications are uncommon in children with SARS-CoV-2 infection and observed with underlying co-morbid conditions. Understanding the complete spectrum of hematologic complications in children with SARS-CoV-2 infection or MIS-C requires ongoing multi-center studies.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".