Epicardial and hepatic fat in Fontan patients is associated with ventricular changes on cardiac MRI
Bibliographic record
Abstract
Background Epicardial and hepatic fat are suspected to play a role in cardiac remodeling. The primary objective of this study was to identify the cardiac MRI (CMR) parameters that are associated with indexed epicardial fat volume (EFV i ) and hepatic steatosis in patients after the Fontan operation. Methods This was a single-center, retrospective analysis of Fontan patients. Epicardial and subcutaneous fat were analyzed with CMR post-processing software, cvi42 (Circle Cardiovascular Imaging, Calgary, Alberta, Canada). Hepatic steatosis was measured with controlled attenuation parameter (CAP) scores via vibration controlled transient elastography. Results The cohort included 81 patients (64% male, median age 16 years). On univariate analysis, EFV i correlated with BMI (ρ=0.52, p<0.001), age (ρ=0.48, p<0.001), EDV i (ρ=0.40, p<0.001), ventricular mass i (ρ=0.39, p<0.001), subcutaneous fat thickness (ρ=0.31, p<0.01), and inferior vena cava (IVC) flow (ρ=0.31, p<0.05). On multivariable regression analysis, BMI (β 2.16, p<0.001) and EDV i (β 0.30, p<0.001) were independently associated with EFV i (R 2 0.47). Males had higher median CAP scores than females (222 vs 196 dB/m; p<0.05). On univariate analysis, CAP scores correlated with BMI (ρ=0.53, p<0.001), subcutaneous fat thickness (ρ=0.48, p<0.001), age (ρ=0.35, p<0.01), EFV i (ρ=0.34, p<0.01), and IVC flow (ρ=0.29, p=0.02). On multivariable analysis, BMI (β 5.8, p<0.001) was independently associated with CAP scores (R 2 =0.28). There were no significant relationships of EFV i or CAP with adverse clinical events. Conclusion BMI is a strong predictor of hepatic and epicardial fat distribution. Epicardial fat volume is also associated with ventricular dilation and may play a role in adverse cardiac remodeling.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".