MétaCan
Menu
Back to cohort
Record W3195688352 · doi:10.1016/j.ahj.2021.08.003

The NHLBI Study on Long-terM OUtcomes after the Multisystem Inflammatory Syndrome In Children (MUSIC): Design and Objectives

2021· article· en· W3195688352 on OpenAlexaff
Dongngan T. Truong, Felicia Trachtenberg, Gail D. Pearson, Audrey Dionne, Matthew D. Elias, Kevin G. Friedman, Kerri Hayes, Lynn Mahony, Brian W. McCrindle, Matthew E. Oster, Victoria L. Pemberton, Andrew J. Powell, Mark W. Russell, Lara Shekerdemian, Mary Beth F. Son, Michael D. Taylor, Jane W. Newburger, Therese M. Giglia, Kimberly E. McHugh, Andrew M. Atz, Scott Pletzer, Sean M. Lang, R. Mark Payne, Jyoti Patel, Ricardo H. Pignatelli, Kristen Sexson, Christopher Wai Kei Lam, Andréea Dragulescu, Rae SM Young, Beth Gamulka, Anita Krishnan, Brett R. Anderson, Kanwal M. Farooqi, Divya Shakti, Aimee S. Parnell, Onyekachukwu Osakwe, Michelle Sykes, Lerraughn Morgan, Carl Owada, Daniel Forsha, Michael R. Carr, Kae Watanabe, Michael A. Portman, Kristen B. Dummer, Jane C. Burns, Adriana H. Tremoulet, Kavita Sharma, Pei‐Ni Jone, Michelle Hite Heather Heizer, Keren Hasbani, Shubhika Srivastava, Elizabeth Mitchell, Camden L. Hebson, Jacqueline Szmuszkovicz, Pierre C. Wong, Andrew L. Cheng, Jodie K. Votava‐Smith, Shuo Wang, Sindhu Mohandas, Gautam K. Singh, Sanjeev Aggarwal, Yamuna Sanil, Tamara T. Bradford, Juan Carlos Muniz, Jennifer S. Li, Michael J. Campbell, Stephanie S. Handler, J. Ryan Shea, Timothy M. Hoffman, Wayne Franklin, Arash Sabati, Todd Nowlen, Maryanne Chrisant

Bibliographic record

VenueAmerican Heart Journal · 2021
Typearticle
Languageen
FieldMedicine
TopicKawasaki Disease and Coronary Complications
Canadian institutionsSickKids FoundationHospital for Sick ChildrenUniversity of Toronto
FundersEunice Kennedy Shriver National Institute of Child Health and Human DevelopmentNational Heart, Lung, and Blood InstituteNational Institutes of HealthNational Institute of Neurological Disorders and StrokeU.S. Department of Health and Human Services
KeywordsMedicineEjection fractionAcute coronary syndromeObservational studyCardiac magnetic resonance imagingCohortCohort studyCoronary artery diseaseInternal medicineCardiologyPediatricsIntensive care medicineMagnetic resonance imagingHeart failureMyocardial infarctionRadiology

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)0.007
metaresearch head score (Gemma)0.005
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: Protocol · Consensus signal: none
Teacher disagreement score0.007
Threshold uncertainty score0.036

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.005
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0020.003
Science and technology studies0.0010.001
Scholarly communication0.0010.002
Open science0.0020.002
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.023
GPT teacher head0.306
Teacher spread0.282 · 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
GenreProtocol

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

Citations31
Published2021
Admission routes1
Has abstractno

Explore more

Same venueAmerican Heart JournalSame topicKawasaki Disease and Coronary ComplicationsFrench-language works237,207