Epicardial Fat in Heart Failure with Preserved Ejection Fraction Compared with Reduced Ejection Fraction
Bibliographic record
Abstract
Background: The role of epicardial adipose tissue (EAT) in heart failure with preserved ejection fraction (HFpEF) remains to be defined. Methods: A consecutive series of outpatients with chronic heart failure—heart failure with reduced ejection fraction (HFrEF) and HFpEF and/or diastolic dysfunction—had EAT assessed by echocardiographic measurement and related to indices of cardiac structure and function. Results: Epicardial fat thickness was significantly (p < 0.05) greater in HFpEF (N = 141) with a mean of 6.7 ± 1.6 mm compared with a mean of 5.1 ± 1.0 mm in HFrEF (n = 40). After adjusting for the relationship with BMI, in HFpEF, epicardial fat was significantly (p < 0.05) negatively correlated with left ventricular internal diameter end diastole (LVIDd), left ventricular internal diameter end systole (LVIDs), left ventricular (LV) end-diastolic volume (EDV) index, lateral e’, septal e’, right atrial (RA) volume index, and hemoglobin (Hgb). The association with Hgb was no longer significant after adjusting for the effect of age. HFpEF was associated with smaller LVIDd, LVIDs, LV EDV indexes, and left atrial (LA) and RA volume indexes. Conclusions: Epicardial fat is significantly (p < 0.05) greater in HFpEF than HFrEF. Epicardial fat is associated with smaller cardiac chamber sizes in HFpEF suggesting that epicardial fat acts as a constraint to cardiac dilation.
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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 | 0.002 |
| 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) | 0.002 | 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".