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Record W4283216637 · doi:10.1186/s12968-022-00843-7

Society for Cardiovascular Magnetic Resonance/European Society of Cardiovascular Imaging/American Society of Echocardiography/Society for Pediatric Radiology/North American Society for Cardiovascular Imaging Guidelines for the use of cardiovascular magnetic resonance in pediatric congenital and acquired heart disease

2022· review· en· W4283216637 on OpenAlexaboutno aff
Mark A. Fogel, Shaftkat Anwar, Craig S. Broberg, Lorna P. Browne, Taylor Chung, Tiffanie R. Johnson, Vivek Muthurangu, Michael D. Taylor, Emanuela Valsangiacomo-Buechel, Carolyn M. Wilhelm

Bibliographic record

VenueJournal of Cardiovascular Magnetic Resonance · 2022
Typereview
Languageen
FieldMedicine
TopicCongenital Heart Disease Studies
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineExpansiveMagnetic resonance imagingAngiologyCanadian Cardiovascular SocietyCardiac magnetic resonance imagingCardiac magnetic resonanceIntensive care medicineRadiologyInternal medicine

Abstract

fetched live from OpenAlex

Cardiovascular magnetic resonance (CMR) has been utilized in the management and care of pediatric patients for nearly 40 years. It has evolved to become an invaluable tool in the assessment of the littlest of hearts for diagnosis, pre-interventional management and follow-up care. Although mentioned in a number of consensus and guidelines documents, an up-to-date, large, stand-alone guidance work for the use of CMR in pediatric congenital 36 and acquired 35 heart disease endorsed by numerous Societies involved in the care of these children is lacking. This guidelines document outlines the use of CMR in this patient population for a significant number of heart lesions in this age group and although admittedly, is not an exhaustive treatment, it does deal with an expansive list of many common clinical issues encountered in daily practice.

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)0.003
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 score0.008
Threshold uncertainty score0.027

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0060.004
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0080.004

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 head0.051
GPT teacher head0.295
Teacher spread0.244 · 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

Citations67
Published2022
Admission routes1
Has abstractyes

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