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Record W3137789078 · doi:10.21203/rs.3.rs-33772/v1

Inflammation and Endothelial Injury Profiling in COVID-19 Pediatric Multi-system Inflammatory Syndrome

2020· preprint· en· W3137789078 on OpenAlexafffund
Douglas D. Fraser, Eric K. Patterson, Mark Daley, Gediminas Cepinskas

Bibliographic record

VenueResearch Square (Research Square) · 2020
Typepreprint
Languageen
FieldMedicine
TopicKawasaki Disease and Coronary Complications
Canadian institutionsWestern University
FundersLondon Health Sciences FoundationAcademic Medical Organization of Southwestern OntarioLawson Health Research Institute
KeywordsCoronavirus disease 2019 (COVID-19)Profiling (computer programming)InflammationMedicine2019-20 coronavirus outbreakSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)VirologyInternal medicineComputer scienceOutbreakInfectious disease (medical specialty)Disease

Abstract

fetched live from OpenAlex

Abstract Background COVID-19 is associated with a novel multi-system inflammatory syndrome that shares some characteristics with Kawasaki’s Disease. The syndrome manifestation is delayed relative to COVID-19 onset, with a spectrum of clinical severity. Clinical signs may include persistent fever, gastrointestinal symptoms, cardiac inflammation and/or shock. Case Presentation: We measured 59 inflammatory and endothelial injury plasma analytes in an adolescent girl that presented with malaise, fever, cough, strawberry tongue and jaundice. Her COVID-19 status was positive with detection of 2 SARS-CoV-2 viral genes using polymerase chain reaction. She was treated with intravenous immunoglobulin prior to blood draw, but our plasma measurements suggested a unique analyte expression pattern associated with inflammation, endothelial injury and microvascular glycocalyx degradation. Conclusions COVID-19 is associated with a multi-system inflammatory syndrome and a unique inflammatory and endothelial injury signature. Analyte markers of inflammation and endothelium injury might serve as putative biomarkers and/or be investigated further as potential therapeutic targets.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.009
metaresearch head score (Gemma)0.006
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.153
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0090.006
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0050.003
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0010.004
Research integrity0.0010.007
Insufficient payload (model declined to judge)0.0000.001

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 head0.145
GPT teacher head0.444
Teacher spread0.299 · 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 teacher head, not a consensus.

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".

Quick stats

Citations1
Published2020
Admission routes2
Has abstractyes

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