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Record W2174407839 · doi:10.1586/14779072.2016.1113130

The role of imaging in the diagnosis and management of hypertrophic cardiomyopathy

2015· review· en· W2174407839 on OpenAlexaff
Adaya Weissler‐Snir, Andrew Crean, Harry Rakowski

Bibliographic record

VenueExpert Review of Cardiovascular Therapy · 2015
Typereview
Languageen
FieldMedicine
TopicCardiomyopathy and Myosin Studies
Canadian institutionsToronto General Hospital
Fundersnot available
KeywordsMedicineHypertrophic cardiomyopathyCardiologyMagnetic resonance imagingInternal medicineCardiac magnetic resonance imagingVentricular outflow tract obstructionLeft ventricular hypertrophyCardiomyopathyRisk stratificationMyocardial hypertrophyMyocardial fibrosisMuscle hypertrophyRadiologyFibrosisHeart failure

Abstract

fetched live from OpenAlex

Hypertrophic cardiomyopathy (HCM) is the most common genetic cardiomyopathy, affecting approximately 1:500 people. As the yield of genetic testing is only about 35-60%, the diagnosis of HCM is still clinical and based on the demonstration of unexplained and usually asymmetric left ventricular (LV) hypertrophy by imaging modalities. In the past, echocardiography was the sole imaging modality used for the diagnosis and management of HCM. However, in recent years other imaging modalities such as cardiac magnetic resonance have played a major role in the diagnosis, management and risk stratification of HCM, particularly when the location of left ventricular hypertrophy is atypical (apex, lateral wall) and when the echocardiographic imaging is sub-optimal. However, the most unique contribution of cardiac magnetic resonance is the quantification of myocardial fibrosis. Exercise stress echocardiography is the preferred provocative test for the assessment of LV outflow tract obstruction, which is detected only on provocation in one-third of the patients.

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)0.005
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: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.941
Threshold uncertainty score0.964

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0050.005
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.039
GPT teacher head0.340
Teacher spread0.301 · 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 designOther design
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

Citations11
Published2015
Admission routes1
Has abstractyes

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