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Record W2171022000 · doi:10.1186/1532-429x-10-58

Late gadolinium enhancement cardiovascular magnetic resonance in genotyped hypertrophic cardiomyopathy with normal phenotype

2008· article· en· W2171022000 on OpenAlexaff
Bradford Strijack, Vignendra Ariyarajah, Reeni Soni, Davinder S. Jassal, Cheryl R. Greenberg, Robert McGregor, Andrew L. Morris

Bibliographic record

VenueJournal of Cardiovascular Magnetic Resonance · 2008
Typearticle
Languageen
FieldMedicine
TopicCardiomyopathy and Myosin Studies
Canadian institutionsHealth Sciences CentreUniversity of ManitobaSt. Boniface Hospital
Fundersnot available
KeywordsMedicineHypertrophic cardiomyopathyCardiologyAsymptomaticInternal medicineVentricleAngiologyCardiomyopathyCardiac magnetic resonance imagingMagnetic resonance imagingTroponinCardiac magnetic resonanceRadiologyMyocardial infarctionHeart failure

Abstract

fetched live from OpenAlex

A 35 year-old asymptomatic Caucasian female with a family history of hypertrophic cardiomyopathy (HCM) was referred for cardiologic evaluation. The electrocardiogram and transthoracic echocardiogram were normal. Cardiovascular magnetic resonance (CMR) was performed for further assessment of myocardial function and presence of myocardial scar. CMR showed normal left ventricular systolic size, measurements and function. However, there was extensive, diffuse late gadolinium enhancement (LGE) throughout the left ventricle. This finding was consistent with extensive myocardial scarring and was highly suggestive of advanced, non-ischemic cardiomyopathy. Genotyping showed a heterozygous mis-sense mutation (275G>A) in the cardiac troponin T (TNNT2) gene, which is causally associated with HCM. There have been no previous reports of such extensive, atypical pattern of myocardial scarring despite an otherwise structurally and functionally normal left ventricle in an asymptomatic individual with HCM. This finding has important implications for phenotype screening in HCM.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.883
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.003
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.013
GPT teacher head0.212
Teacher spread0.198 · 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.

Study designOther design
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

Citations33
Published2008
Admission routes1
Has abstractyes

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