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Record W4379618779 · doi:10.1016/j.cjca.2023.06.001

Kawasaki Disease in the Time of COVID-19 and MIS-C: The International Kawasaki Disease Registry

2023· article· en· W4379618779 on OpenAlexafffundvenue
Ashraf S. Harahsheh, Samay Shah, Frédéric Dallaire, Cedric Manlhiot, Michael Khoury, Simon Lee, Marianna Fabi, Daniel Mauriello, Elif Seda Selamet Tierney, Arash Sabati, Audrey Dionne, Nagib Dahdah, Nadine Choueiter, Deepika Thacker, Therese M. Giglia, Dongngan T. Truong, Supriya Jain, Michael A. Portman, William B. Orr, Tyler H. Harris, Pedrom Farid, Brian W. McCrindle, Mahmoud Alsalehi, Jean A. Ballweg, Benjamin Barnes, Elizabeth Braunlin, Ashley Buffone, Juan Carlos Bustamante‐Ogando, Arthur Chang, Nicolas Corral, Paul Dancey, Mona Mostafa El-Ganzoury, Nora Elsamman, Matthew D. Elias, Elisa Fernández-Cooke, Kevin Friedman, Luis Martín Garrido‐García, Luis Martin Garrido, Guillermo Larios Goldenberg, Michelle M. Grcic, Kevin C. Harris, Mark D. Hicar, Bridgette Hindt, Pei‐Ni Jone, Hidemi Kajimoto, Kelli Kaneta, Manaswitha Khare, Stacie Knutson, Shelby Kutty, Marcello Lanari, Victoria Maksymiuk, Kimberly E. McHugh, Shae A. Merves, Nilanjana Misra, Sindhu Mohandas, Tapas Mondal, Kambiz Norozi, Todd Nowlen, Joseph J. Pagano, Deepa Prasad, Geetha Raghuveer, Prasad Ravi, Sundaram Balasubramanian, Anupam Sehgal, Ashish H. Shah, Belén Toral Vázquez, Adriana H. Tremoulet, Aishwarya Venkataraman, Laurence Watelle, Marco Antonio Yamazaki-Naksahimada, Anji T. Yetman

Bibliographic record

VenueCanadian Journal of Cardiology · 2023
Typearticle
Languageen
FieldMedicine
TopicKawasaki Disease and Coronary Complications
Canadian institutionsHospital for Sick ChildrenUniversity of TorontoUniversité de MontréalUniversity of AlbertaCentre Hospitalier Universitaire de SherbrookeUniversité de Sherbrooke
FundersUniversity of California, San DiegoNational Institute of Child Health and Human DevelopmentNational Institutes of HealthNational Institute of Biomedical Imaging and BioengineeringNational Heart, Lung, and Blood InstituteHospital for Sick ChildrenNIH Office of the DirectorEunice Kennedy Shriver National Institute of Child Health and Human DevelopmentBoston Children's HospitalSeattle Children's Research InstituteUniversity of TorontoJohns Hopkins University
KeywordsKawasaki diseaseMedicineCoronavirus disease 2019 (COVID-19)PandemicDisease2019-20 coronavirus outbreakSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)PediatricsInternal medicineVirologyInfectious disease (medical specialty)Outbreak

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.033
Threshold uncertainty score0.065

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.004
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.027
GPT teacher head0.291
Teacher spread0.264 · 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 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

Citations22
Published2023
Admission routes3
Has abstractno

Explore more

Same venueCanadian Journal of CardiologySame topicKawasaki Disease and Coronary ComplicationsFrench-language works237,207