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Record W2974133203 · doi:10.3233/jnd-190415

The Added Value of Cardiac Magnetic Resonance in Muscular Dystrophies

2019· review· en· W2974133203 on OpenAlexaff
M. Lamacie, Jodi Warman‐Chardon, Andrew Crean, Anca Florian, Karim Wahbi

Bibliographic record

VenueJournal of Neuromuscular Diseases · 2019
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMuscle Physiology and Disorders
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsMedicineCardiologyInternal medicineCardiac magnetic resonance imagingMyocardial fibrosisMagnetic resonance imagingHeart failureCardiomyopathyCardiac magnetic resonanceRadiology

Abstract

fetched live from OpenAlex

Muscular dystrophies (MD) represent a heterogeneous group of rare genetic diseases that often lead to significant weakness due to progressive muscle degeneration. In many forms of MD, cardiac manifestations including heart failure, atrial and ventricular arrhythmias and conduction abnormalities can occur and may be a predominant feature of the disease. Cardiac magnetic resonance (CMR) can assess cardiac anatomy, global and regional ventricular function, volumes and mass as well as presence of myocardial inflammation, infiltration or fibrosis. The role for cardiac MRI has been well-established in a wide range of muscular dystrophies related cardiomyopathies. CMR is a more sensitive technique than echocardiography for early diagnosis of cardiac involvement. It has also great potential to improve the prediction of long-term outcome, particularly the development of heart failure and arrhythmic events; however it still has to be validated by longitudinal studies including large populations. This review will outline the utility of CMR in patients with muscular dystrophies for assessment of myocardial involvement, risk stratification, and in guiding therapeutic management.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.989
Threshold uncertainty score0.925

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.002
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 head0.014
GPT teacher head0.277
Teacher spread0.263 · 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 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

Citations23
Published2019
Admission routes1
Has abstractyes

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