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Record W3084139278

COVID-19 and multisystem inflammatory syndrome in children and adolescents

2020· article· en· W3084139278 on OpenAlexaff
Li Jiang, Kun Tang, Michael Levin, Omar Irfan, Shaun K. Morris, Karen M. Wilson, Jonathan D. Klein, Zulfiqar A Bhutta

Bibliographic record

VenueeCommons - AKU (Aga Khan University) · 2020
Typearticle
Languageen
FieldMedicine
TopicKawasaki Disease and Coronary Complications
Canadian institutionsCentre for Global Health ResearchHospital for Sick Children
Fundersnot available
KeywordsMedicineCoronavirus disease 2019 (COVID-19)Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)EpidemiologyPsychological interventionIntensive care medicineCoronavirusPandemic2019-20 coronavirus outbreakPediatricsImmunologyOutbreakVirologyDiseasePathologyPsychiatryInfectious disease (medical specialty)
DOInot available

Abstract

fetched live from OpenAlex

As severe acute respiratory syndrome coronavirus 2 continues to spread worldwide, there have been increasing reports from Europe, North America, Asia, and Latin America describing children and adolescents with COVID-19-associated multisystem inflammatory conditions. However, the association between multisystem inflammatory syndrome in children and COVID-19 is still unknown. We review the epidemiology, causes, clinical features, and current treatment protocols for multisystem inflammatory syndrome in children and adolescents associated with COVID-19. We also discuss the possible underlying pathophysiological mechanisms for COVID-19-induced inflammatory processes, which can lead to organ damage in paediatric patients who are severely ill. These insights provide evidence for the need to develop a clear case definition and treatment protocol for this new condition and also shed light on future therapeutic interventions and the potential for vaccine development.Translations: For the French, Chinese, Arabic, Spanish and Russian translations of the abstract see Supplementary Materials section.

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 score0.007
Threshold uncertainty score0.596

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 head0.015
GPT teacher head0.219
Teacher spread0.204 · 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

Citations13
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueeCommons - AKU (Aga Khan University)Same topicKawasaki Disease and Coronary ComplicationsFrench-language works237,207