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Record W3191522291 · doi:10.46747/cfp.6708594

Post–COVID-19 multisystem inflammatory syndrome in children

2021· article· en· W3191522291 on OpenAlexvenueno aff
Michelle M. Kim, Srinivas Murthy, Ran D. Goldman

Bibliographic record

VenueCanadian Family Physician · 2021
Typearticle
Languageen
FieldMedicine
TopicKawasaki Disease and Coronary Complications
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineAsymptomaticMucocutaneous zoneVomitingPediatricsRashDiarrheaPopulationPandemicIntensive care medicineAbdominal painDiseaseCoronavirus disease 2019 (COVID-19)ImmunologyDermatologyInternal medicineInfectious disease (medical specialty)

Abstract

fetched live from OpenAlex

QUESTION: The effect of acute coronavirus disease 2019 (COVID-19) on morbidity and mortality in children has been relatively small. If a child presents to my office with persistent fever and systemic hyperinflammation but no known exposure to COVID-19, how likely are they to have multisystem inflammatory syndrome in children (MIS-C)? What is currently known about MIS-C and what is the prognosis for children affected by it? ANSWER: Amid the COVID-19 pandemic, the emergence of a novel condition presents yet another challenge to clinicians, public health professionals, and the pediatric population. Multisystem inflammatory syndrome in children is a rare but potentially severe condition seen in children with evidence of COVID-19 approximately 2 to 6 weeks before symptom onset. Common signs and symptoms include persistent fever, systemic hyperinflammation, gastrointestinal symptoms (eg, abdominal pain, vomiting, diarrhea), mucocutaneous changes (eg, rash, conjunctivitis), headache, or cardiac dysfunction. As many children present as asymptomatic or with mild symptoms of COVID-19, the development of MIS-C can seem sudden and surprising to families and providers. Although children with MIS-C usually require hospitalization, the outcomes are largely favourable with prompt recognition and intense therapy.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.128
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

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

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 head0.014
GPT teacher head0.244
Teacher spread0.230 · 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.

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".

Quick stats

Citations8
Published2021
Admission routes1
Has abstractyes

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