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Record W4403660563 · doi:10.1016/j.jocmr.2024.101113

Characterization and z-score calculation of cardiovascular magnetic resonance imaging parameters in patients after the Fontan operation: A Fontan Outcome Registry using Cardiovascular Magnetic Resonance Examinations study

2024· article· en· W4403660563 on OpenAlexaff
Tarek Alsaied, Runjia Li, Adam Christopher, Mark A. Fogel, Timothy C. Slesnick, Rajesh Krishnamurthy, Vivek Muthurangu, Adam L. Dorfman, Christopher Z. Lam, Justin Weigand, Jong‐Hyeon Jeong, Joshua D. Robinson, Laura Olivieri, Rahul H. Rathod

Bibliographic record

VenueJournal of Cardiovascular Magnetic Resonance · 2024
Typearticle
Languageen
FieldMedicine
TopicCongenital Heart Disease Studies
Canadian institutionsSickKids Foundation
FundersNational Institutes of HealthNational Center for Advancing Translational SciencesAdditional VenturesNational Institute of Health Sciences
KeywordsMedicineAngiologyFontan procedureInternal medicineCardiologyCardiac magnetic resonanceOutcome (game theory)RadiologyMagnetic resonance imagingHeart disease

Abstract

fetched live from OpenAlex

Cardiac magnetic resonance (CMR) offers valuable hemodynamic insights post-Fontan, but is limited by the absence of normative single ventricle data. The Fontan Outcomes Registry using CMR Examinations (FORCE) is a large international Fontan-specific CMR registry. This study used FORCE registry data to evaluate expected CMR ventricular size/function and create Fontan-specific z-scores adjusting for ventricular morphology (VM) in healthier Fontan patients. “Healthier” Fontan patients were defined as patients free of adverse outcomes, who are New York Heart Association class I, have mild or less valve disease, and <30% aortopulmonary collateral burden. General linear modeling was performed on 70% of the dataset to create z-scores for volumes and function. Models were tested using the remainder (30%) of the data. The z-scores were compared between children and adults. The z-scores were also compared between “healthier” Fontan and patients with adverse outcomes (death, listing for transplantation, or multiorgan disease). The “healthier” Fontan population included 885 patients (15.0 ± 7.6 years) from 18 institutions with 1156 CMR examinations. Patients with left ventricle morphology had lower volume, mass and higher ejection fraction (EF) compared to right or mixed (two ventricles) morphology (p < 0.001 for all pairwise comparisons). Gender, body surface area, and VM were used in z-scores. Of the “healthier” Fontan patients, 647 were children <18 years and 238 were adults. Adults had lower ascending aorta flow (2.9 ± 0.7 vs 3.3 ± 0.8 L/min/m 2 , p < 0.001) and ascending aorta flow z-scores (−0.16 ± 1.23 vs 0.05 ± 0.95, 0.02) compared to children. Additionally, there were 1595 patients with adverse outcomes who were older (16.1 ± 9.3 vs 15.0 ± 7.6, p < 0.001) and less likely to have left VM (35 vs 47%, p < 0.001). Patients with adverse outcomes had higher z-scores for ventricular volume and mass and lower z-scores for EF and ascending aorta flow compared to the “healthier” Fontan cohort. This is the first study to generate CMR z-scores post-Fontan. Importantly, the z-scores were generated and tested in “healthier” Fontan patients and both pediatric and adult Fontan patients. These equations may improve CMR-based risk stratification after the Fontan operation. Graphical abstract showing the differences in ventricular volume and function between different study groups.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.003
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.017
GPT teacher head0.253
Teacher spread0.236 · 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 source (direct Gemma or distilled Codex), 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

Citations8
Published2024
Admission routes1
Has abstractyes

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