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Record W4416717347 · doi:10.1186/s12880-025-02026-z

Post-valvular energy exchange in bicuspid aortic valve with regurgitation and stenosis and its association with left ventricular workload: a 4D flow magnetic resonance imaging cross-sectional study

2025· article· en· W4416717347 on OpenAlex
Shirin Aliabadi, Dina Labib, Sandra Rivest, James A. White, Julio García

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".

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

VenueBMC Medical Imaging · 2025
Typearticle
Languageen
FieldMedicine
TopicCardiac Valve Diseases and Treatments
Canadian institutionsAlberta Children's HospitalLibin Cardiovascular Institute of AlbertaUniversity of Calgary
FundersNatural Sciences and Engineering Research Council of CanadaCircle Cardiovascular ImagingCalgary Health FoundationCanadian Institutes of Health ResearchAlberta InnovatesAlberta Innovates - Health Solutions
KeywordsBicuspid aortic valveRegurgitation (circulation)StenosisMagnetic resonance imagingCardiac magnetic resonanceAortic valveFlow (mathematics)

Abstract

fetched live from OpenAlex

BACKGROUND: Bicuspid aortic valve (BAV) regurgitation and stenosis considerably alter post-valvular flow dynamics and impose additional energetic load on the left ventricle (LV). We therefore sought to determine whether 4D Flow MRI-derived ascending-aortic kinetic energy (KE) and viscous energy loss (EL), can differentiate BAV subtypes and healthy controls, and are associated with LV remodeling markers. METHODS: Seventy-one participants (19 BAV without valve dysfunction, 17 with isolated aortic regurgitation (BAV-AR), 15 with isolated aortic stenosis (BAV-AS), and 20 healthy controls) underwent 3.0 T magnetic resonance imaging (MRI), including cine balanced SSFP and 4D-Flow. Post valvular KE, viscous EL, and the dimensionless EL index were computed from the 4D Flow velocity fields. Global 3D LV strain metrics were derived via cine SSFP feature-tracking technique. Between-group differences were assessed with one-way ANOVA or Kruskal-Wallis tests, and associations were evaluated using Spearman's rank correlation. RESULTS: Average ascending aortic KE rose progressively from controls (3.3[2.3-4.3]) to uncomplicated BAV (6.7[5.3-9.1]), to BAV-AS (15.4[12.2-29.5]) and peaked in the BAV-AR (19.4[14.9-21.3], p < 0.001). Peak-systolic viscous EL was significantly elevated in both the stenotic (16.2 [9.1-24.4] mW) and regurgitant (11.4 [9.5-17.6] mW) groups compared to controls (4.1 [3.4-5.7] mW), but not in the uncomplicated BAV (6.4 [5.1-8.0] mW). Over the entire systole, viscous EL in the uncomplicated BAV (3.3 [2.5-4.1] mW) was also statistically increased compared to controls (1.7 [1.3-2.3] mW). KE correlated more strongly with regurgitation severity (rho = 0.50, p < 0.001), and EL with stenosis severity (rho = 0.48, p < 0.001). Aortic surgery referral was more closely associated with elevated KE (rho = 0.65, p < 0.001) and viscous EL (rho = 0.64, p < 0.001) than with aortic diameter (rho = 0.50, p < 0.001). Left ventricular Mass index and peak diastolic strain rate circumferential were correlated but more strongly with KE than viscous EL. CONCLUSIONS: 4D Flow MRI-derived post-valvular KE and viscous EL may serve as sensitive early biomarkers of LV dysfunction, and might outperform aortic diameter in risk stratification, and guide optimal intervention timing in BAV diseases while they need to be validated in broader populations.

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)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.029
Threshold uncertainty score0.677

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.006
GPT teacher head0.293
Teacher spread0.286 · 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