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Record W2597105361 · doi:10.1161/circresaha.116.308983

Noninvasive Imaging in Adult Congenital Heart Disease

2017· review· en· W2597105361 on OpenAlexaff
Luke J. Burchill, Jennifer Huang, Justin T. Tretter, Abigail Khan, Andrew Crean, Gruschen Veldtman, Sanjiv Kaul, Craig S. Broberg

Bibliographic record

VenueCirculation Research · 2017
Typereview
Languageen
FieldMedicine
TopicCongenital Heart Disease Studies
Canadian institutionsToronto General Hospital
FundersSociety for Cardiovascular Magnetic Resonance
KeywordsMedicineMagnetic resonance imagingCardiac imagingHeart diseaseCardiologyCardiac magnetic resonance imagingInternal medicineDiseaseRadiologyMedical imaging

Abstract

fetched live from OpenAlex

Multimodality cardiovascular imaging plays a central role in caring for patients with congenital heart disease (CHD). CHD clinicians and scientists are interested not only in cardiac morphology but also in the maladaptive ventricular responses and extracellular changes predisposing to adverse outcomes in this population. Expertise in the applications, strengths, and pitfalls of these cardiovascular imaging techniques as they relate to CHD is essential. The purpose of this article is to provide an overview of cardiovascular imaging in CHD. We focus on the role of 3 widely used noninvasive imaging techniques in CHD-echocardiography, cardiac magnetic resonance imaging, and cardiac computed tomography. Consideration is given to the common goals of cardiac imaging in CHD, including assessment of structural and residual heart disease before and after surgery, quantification of ventricular volume and function, stress imaging, shunt quantification, and tissue characterization. Extracardiac imaging is highlighted as an increasingly important aspect of CHD care.

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)0.001
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.953
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.001

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 head0.234
GPT teacher head0.497
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.

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

Citations46
Published2017
Admission routes1
Has abstractyes

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