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Record W2135140903 · doi:10.1093/ehjci/jes314

Usefulness of cardiovascular magnetic resonance imaging for the evaluation of valve opening and closing kinetics in aortic stenosis

2013· article· en· W2135140903 on OpenAlexafffund
Julio García, Philippe Pîbarot, Romain Capoulade, Florent Le Ven, Lyes Kadem, Éric Larose

Bibliographic record

VenueEuropean Heart Journal - Cardiovascular Imaging · 2013
Typearticle
Languageen
FieldMedicine
TopicCardiac Valve Diseases and Treatments
Canadian institutionsConcordia UniversityUniversité LavalInstitut universitaire de cardiologie et de pneumologie de Québec
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health Research
KeywordsClosing (real estate)StenosisMagnetic resonance imagingAortic valveCardiologyMedicineInternal medicineAortic valve stenosisNuclear magnetic resonanceRadiologyPhysicsBusiness

Abstract

fetched live from OpenAlex

AIMS: The aims of this study were : (i) to determine the feasibility and reproducibility of the measurement of valve kinetic parameters by cardiovascular magnetic resonance (CMR) and (ii) to examine the association between these parameters and markers of a poor prognosis in patients with aortic stenosis (AS). METHODS AND RESULTS: Eight healthy control subjects and 71 patients with AS (0.60 cm(2) ≤ EOA ≤ 1.90 cm(2)) underwent transthoracic echocardiography (TTE) and CMR. The valve opening slope (OS) and closing slope (CS) were calculated from instantaneous effective orifice area (EOA) curves obtained by CMR. Intra- and inter-observer variability were 4.8 ± 3.9 and 5.0 ± 4.1%, respectively, for OS, 3.8 ± 2.9 and 4.0 ± 3.1% for CS. OS was significantly related to the plasma level of NT-pro-brain natriuretic peptide (BNP) (r = -0.36, P = 0.002), whereas the EOA or gradient were not. CONCLUSION: This study demonstrates the excellent feasibility and reproducibility of CMR for the measurement of valve kinetic parameters in patients with AS. Larger studies are needed to confirm the incremental prognostic value of these new CMR parameters of aortic valve kinetics in patients with severe AS.

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)0.004
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.621
Threshold uncertainty score0.754

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.004
Bibliometrics0.0000.000
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 head0.045
GPT teacher head0.321
Teacher spread0.276 · 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 designObservational
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

Citations12
Published2013
Admission routes2
Has abstractyes

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