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Hematologic manifestations of SARS-CoV-2 infection and MIS-C in hospitalized children. Results of the PICNIC registry.

2022· preprint· en· W4205125958 on OpenAlexaff
Sarah Tehseen, Suzan Williams, Joan Robinson, Shaun K. Morris, Tala Tal, Ari Bitnun, Peter J. Gill, E. Ann Yeh, Carmen Yea, Helena Brenes, Adrianna Yock-Corrales, R. M. Sarrat Nuevo, Gabriela Ivankovich-Esctoto, Alejandra Soriano Fallas, Jesse Papenburg, Marie‐Astrid Lefebvre, Rosie Scuccimarri, Alireza Nateghian, Behzad Haghighi Aski, Rachel Dwilow, Jared Bullard, Leo Restivo, Suzette Cooke, Alison Lopez, Ashley Roberts, Manish Sadarangani, Michelle Barton Forbes, Nicole Le Saux, Jennifer Bowes, Rupeena Purewal, Janell Lautermilch, Ann Bayliss, Jacqueline Wong, Kirk Leifso, Cheryl Foo, Luc Panetta, Fatima Kakkar, Dominique Piché, Isabelle Viel‐Thériault, Joanna Merckx, Lani Lieberman

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldMedicine
TopicKawasaki Disease and Coronary Complications
Canadian institutionsUniversity Health NetworkUniversité LavalDalhousie UniversityMemorial University of NewfoundlandQueen's UniversityMcMaster UniversityMcGill University Health CentreMcGill UniversityUniversity of OttawaLondon Health Sciences CentreTrillium Health CentreUniversity of ManitobaUniversity of CalgarySickKids FoundationUniversity of British ColumbiaUniversité de MontréalUniversity of SaskatchewanHospital for Sick ChildrenUniversity of Alberta
Fundersnot available
KeywordsMedicinePediatricsInternal medicineRetrospective cohort studyCytokine stormThrombosisDiseaseCoronavirus disease 2019 (COVID-19)Infectious disease (medical specialty)

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.035
GPT teacher head0.323
Teacher spread0.288 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations0
Published2022
Admission routes1
Has abstractyes

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