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Record W4281550823 · doi:10.7573/dic.2022-2-1

Drugs for paediatric hyperinflammatory syndromes

2022· review· en· W4281550823 on OpenAlexaff
Kam‐Lun Ellis Hon, Alexander K. C. Leung, Wing Leung, Karen Ka Yan Leung, Kai Ning Cheong, Pamela PW Lee

Bibliographic record

VenueDrugs in Context · 2022
Typereview
Languageen
FieldMedicine
TopicAutoimmune and Inflammatory Disorders Research
Canadian institutionsAlberta Children's HospitalUniversity of Calgary
Fundersnot available
KeywordsMedicineMacrophage activation syndromeCytokine release syndromeCytokine stormSystemic inflammatory response syndromeImmunologySepsisIntensive care medicinePediatricsInternal medicineDiseaseArthritisCoronavirus disease 2019 (COVID-19)Immune systemT cell

Abstract

fetched live from OpenAlex

Background: Many syndromes are associated with exaggerated inflammation. Children with hyperinflammatory syndromes often present with vague and non-specific symptoms that pose diagnostic and management challenges. The recent literature seems biased towards referring these syndromes only to the multisystem inflammatory syndrome in children (MIS-C) that is associated with COVID-19. The purpose of this paper is to provide an updated narrative review on the pathophysiology, manifestations and management approaches for common hyperinflammatory syndromes. Methods: An extensive PubMed search of all publications in the English literature was performed with Clinical Queries for various hyperinflammatory syndromes and conditions using the undermentioned Medical Subject Headings: "hyperinflammation", "hyperinflammatory syndromes", "sepsis syndrome", "severe inflammatory response syndrome" and "acute respiratory distress syndrome". Categories were limited to reviews and clinical trials for the age range from birth to 18 years. Results: The criteria, presentation and management of these hyperinflammatory syndromes are described. Hyperinflammatory syndromes refer to a basket of inflammatory syndromes often associated with multisystem involvement and aberrant cytokine release and should be differentiated from autoinflammatory, autoimmune and hyperimmune syndromes. The major subtypes of hyperinflammatory syndromes, including macrophage activation syndrome, haemophagocytic lymphohistiocytosis, cytokine release syndrome and cytokine storm syndrome, are described. MIS-C associated with SARS-CoV-2 represents the latest addition. It must be understood that the syndrome is not exclusive to COVID-19 but could be caused by various viral infections. Early recognition, prompt and proactive treatment can reduce potential complications and improve outcomes and survival rates in paediatric patients. Anti-inflammatory medications for the management of these syndromes are described. Conclusion: The incidence of these hyperinflammatory conditions is generally low in comparison to other disease conditions. Except for paediatric inflammatory multisystem syndrome/MIS-C, the mortality is high and the hospital stay is prolonged in affected patients. Acute and critical care physicians must be aware of these conditions and their initial management. Corticosteroids are often used in the initial phrase but various disease-specific drugs and biologics are needed in subsequent management and expert management of these often-difficult conditions is crucial.

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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.015
Threshold uncertainty score0.049

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0150.004

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 head0.063
GPT teacher head0.356
Teacher spread0.292 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations5
Published2022
Admission routes1
Has abstractyes

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