MétaCan
Menu
Back to cohort
Record W4380081486 · doi:10.3390/jcdd10060251

Clinical Use of Blood Flow Analysis through 4D-Flow Imaging in Aortic Valve Disease

2023· review· en· W4380081486 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueJournal of Cardiovascular Development and Disease · 2023
Typereview
Languageen
FieldMedicine
TopicCardiac Valve Diseases and Treatments
Canadian institutionsLibin Cardiovascular Institute of AlbertaAlberta Children's HospitalUniversity of Calgary
FundersSiemens HealthineersUniversity of Calgary
KeywordsBicuspid aortic valveMedicineCardiologyInternal medicineRegurgitation (circulation)StenosisAortic valveMagnetic resonance imagingBlood flowAortaPopulationBicuspid valveAortic valve regurgitationRadiology

Abstract

fetched live from OpenAlex

Bicuspid aortic valve (BAV), which affects 1% of the general population, results from the abnormal fusion of the cusps of the aortic valve. BAV can lead to the dilatation of the aorta, aortic coarctation, development of aortic stenosis (AS), and aortic regurgitation. Surgical intervention is usually recommended for patients with BAV and bicuspid aortopathy. This review aims to examine 4D-flow imaging as a tool in cardiac magnetic resonance imaging for assessing abnormal blood flow and its clinical application in BAV and AS. We present a historical clinical approach summarizing evidence of abnormal blood flow in aortic valve disease. We highlight how abnormal flow patterns can contribute to the development of aortic dilatation and novel flow-based biomarkers that can be used for a better understanding of the disease progression.

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.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Meta-epidemiology (broad)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.617
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0050.029
Bibliometrics0.0010.001
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 head0.076
GPT teacher head0.394
Teacher spread0.318 · 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