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Record W3130300107 · doi:10.1016/j.vaccine.2021.01.054

Multisystem inflammatory syndrome in children and adults (MIS-C/A): Case definition & guidelines for data collection, analysis, and presentation of immunization safety data

2021· review· en· W3130300107 on OpenAlexaff
Tiphanie P. Vogel, Karina A. Top, Christos Karatzios, David C. Hilmers, Lorena Tapia, Pamela Moceri, Lisa Giovannini‐Chami, Nicholas Wood, Rebecca E. Chandler, Nicola P. Klein, Elizabeth P. Schlaudecker, M. Cecilia Poli, Eyal Muscal, Flor M. Muñoz

Bibliographic record

VenueVaccine · 2021
Typereview
Languageen
FieldMedicine
TopicKawasaki Disease and Coronary Complications
Canadian institutionsMcGill University Health CentreDalhousie University
FundersEunice Kennedy Shriver National Institute of Child Health and Human Development
KeywordsImmunizationContext (archaeology)MedicinePreparednessPresentation (obstetrics)Data collectionPediatricsMedical emergencyImmunologyPolitical scienceSurgeryHistory

Abstract

fetched live from OpenAlex

This is a Brighton Collaboration Case Definition of the term "Multisystem Inflammatory Syndrome in Children and Adults (MIS-C/A)" to be utilized in the evaluation of adverse events following immunization. The case definition was developed by topic experts convened by the Coalition for Epidemic Preparedness Innovations (CEPI) in the context of active development of vaccines for SARS-CoV-2. The format of the Brighton Collaboration was followed, including an exhaustive review of the literature, to develop a consensus definition and defined levels of certainty. The document underwent peer review by the Brighton Collaboration Network and by selected expert external reviewers prior to submission. The comments of the reviewers were taken into consideration and edits incorporated into this final manuscript.

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)0.072
metaresearch head score (Gemma)0.172
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Reporting · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.928
Threshold uncertainty score0.383

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0720.172
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0030.003
Bibliometrics0.0180.010
Science and technology studies0.0020.003
Scholarly communication0.0050.005
Open science0.0040.004
Research integrity0.0050.005
Insufficient payload (model declined to judge)0.0070.008

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 head0.137
GPT teacher head0.402
Teacher spread0.265 · 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.

Study designNot applicable
DomainReporting
GenreMethods

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

Citations236
Published2021
Admission routes1
Has abstractyes

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