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Record W2730390323 · doi:10.1097/bor.0000000000000425

Revisiting the role of steroids and aspirin in the management of acute Kawasaki disease

2017· review· en· W2730390323 on OpenAlexafffund
Anita Dhanrajani, Rae S. M. Yeung

Bibliographic record

VenueCurrent Opinion in Rheumatology · 2017
Typereview
Languageen
FieldMedicine
TopicKawasaki Disease and Coronary Complications
Canadian institutionsUniversity of TorontoSickKids FoundationHospital for Sick Children
FundersHospital for Sick Children
KeywordsKawasaki diseaseMedicineAspirinSystemic vasculitisCoronary artery aneurysmVasculitisDiseaseMucocutaneous Lymph Node SyndromeIntensive care medicineInternal medicineArteryPediatrics

Abstract

fetched live from OpenAlex

PURPOSE OF REVIEW: Kawasaki disease is an acute multisystem childhood vasculitis with a predilection for the coronary arteries. The role of corticosteroids and acetyl salicylic acid (ASA) in the treatment of acute Kawasaki disease are matters of ongoing debate and changing attitudes from one extreme to the other. Recent work has provided new evidence to guide our thinking about these two therapeutic agents, which will be the focus of this review. RECENT FINDINGS: Corticosteroids are effective and well tolerated in Kawasaki disease, both as initial adjunctive treatment in those at high-risk for poor outcome, and as rescue therapy after failed intravenous immunoglobulin (IVIG).Higher doses of ASA (> 30 mg/kg/day) in the acute phase of Kawasaki disease, have no clear benefit over antiplatelet doses in improving coronary outcome. SUMMARY: Corticosteroids should be used in patients at high-risk for poor coronary outcome, and in patients who fail IVIG. The absence of widely applicable and validated risk-scoring systems in Kawasaki disease outside of Japan remains a limiting factor to identify high-risk children. Current evidence does not demonstrate any advantage of high-dose over low-dose ASA in the acute phase of Kawasaki disease, in preventing coronary artery aneurysms.

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: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.898
Threshold uncertainty score0.530

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.122
GPT teacher head0.439
Teacher spread0.317 · 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 designOther design
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

Citations16
Published2017
Admission routes2
Has abstractyes

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