A single slice measure of epicardial adipose tissue can serve as an indirect measure of total epicardial adipose tissue burden and is associated with obstructive coronary artery disease
Bibliographic record
Abstract
AIMS: To evaluate the practical use of the single slice measurement of epicardial adipose tissue (EAT) at the level of the left main coronary artery (EATLM) in predicting the presence of obstructive coronary artery disease (CAD). METHODS AND RESULTS: Quantification of EATTotal and EATLM was performed on non-contrast CT scans of consecutive patients (without history of revascularization, cardiac transplantation, device implantation, and congenital heart disease) who underwent coronary artery calcium (CAC) scoring and computed tomographic coronary angiography (CTA) between May 2011 and July 2011. One hundred and ninety-two patients were evaluated, of which 47 had obstructive CAD (>50% stenosis). EATLM (3.8 ± 2.2 cm(3)) and EATTotal (126.2 ± 56.3 cm(3)) are highly correlated (r = 0.89, P < 0.001). Multivariate analysis revealed that both EATLM (OR: 1.204 per 1 cm(3), 95% CI: 1.028-1.411, P = 0.021) and EATTotal (OR: 1.007 per 10 cm(3), 95% CI: 1.000-1.013, P = 0.038) are associated with obstructive CAD. However, when the CAC score was added to multivariate analysis, both failed to show statistical significance. (EATTotal, OR 1.004 per 1 cm(3), 95% CI: 0.996-1.011, P = 0.328 and EATLM, OR: 1.136 per 10 cm(3), 95% CI: 0.948-1.362) ROC curve analysis revealed that both EATTotal and EATLM are of incremental value in detecting CAD, when compared with clinical risk scores (NCEP plus EATTotal plus BMI and NCEP plus EATLM plus BMI vs. NCEP alone; AUC 0.7090, P = 0.009 and 0.7167, P = 0.003 vs. 0.6069, respectively). CONCLUSION: Measuring epicardial adipose tissue on a single slice at the level of the left main coronary artery may serve as an indirect measure of total epicardial adipose tissue burden. EATLM and EATTotal are independently associated with obstructive coronary artery disease and are incremental to traditional risk factors for predicting its presence.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".