Echocardiographic assessment of epicardial adipose tissue thickness as independent predictor in coronary artery disease
Bibliographic record
Abstract
This study aimed to assess the utility of echocardiography-measured epicardial adipose tissue (EAT) thickness (EATT) as an independent predictor for coronary artery disease (CAD), examining its correlation with oxidative stress levels in epicardial tissue and the complexity of the disease in patients undergoing open-heart surgery. This study included a total of 25 patients referred for cardiac surgery with 14 in the CAD group and 11 in the non-CAD group. Epicardial fat was sampled from patients subjected to open-heart surgery . EATT was higher in the CAD group compared to the non-CAD group (8.15 ± 2.09 mm vs. 5.12 ± 1.8 mm, p = 0.001). The epicardial reactive oxygen species level was higher in the CAD group compared to the non-CAD group (21.4 ± 2.47 nmol H2O2/g tisssue/h vs. 15.7 ± 1.55 nmol H2O2/g tisssue/h, p < 0.001). EATT greater than 6.05 mm was associated with CAD, with a sensitivity of 86% and specificity of 73%. Echocardiographically measured EATT is a significant, independent predictor of CAD. Its relationship with increased EAT oxidative stress levels suggests a potential mechanistic link between EATT and CAD pathogenesis. These findings highlight the importance of EATT as a diagnostic tool in assessing the complexity of CAD in patients undergoing cardiac surgery.
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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 | 0.001 | 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) | 0.001 | 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".