Exploring the Relationship between Epicardial Fat Thickness and Coronary Revascularization: Implications for Cardiovascular Health
Bibliographic record
Abstract
BACKGROUND: this study aimed to assess the complex relationship between EAT thickness, as measured with echocardiography, and the severity of coronary artery disease (CAD). We investigated whether individuals with higher EAT thickness underwent coronary revascularization. Subsequently, we conducted a three-year follow-up to explore any potential modifications in EAT depots post-angioplasty. METHODS: we conducted a prospective and retrospective cross-sectional observational study involving 150 patients consecutively referred for acute coronary syndrome, including ST-elevation myocardial infarction (STEMI), non-ST elevation myocardial infarction (NSTEMI), and unstable angina. Upon admission (T0), all patients underwent coronary angiography to assess the number of pathologic coronary vessels. Percutaneous transluminal coronary angioplasty (PTCA) was performed based on angiogram results if indicated. The sample was categorized into two groups: non-revascularized (no-PTCA) and revascularized (PTCA). Transthoracic echocardiograms to measure epicardial fat thickness were conducted at admission (T0) and after a 3-year follow-up (T1). RESULTS AND CONCLUSIONS: findings revealed a positive correlation between EAT thickness and the severity of coronary artery disease (CAD), with patients undergoing PTCA showing decreased EAT thickness after three years. Echocardiography demonstrated reliability in assessing EAT, offering potential for risk stratification. The study introduces a cut-off value of 0.65 cm as a diagnostic tool for cardiovascular risk. Incorporating EAT measurements into clinical practice may lead to more precise risk stratification and tailored treatment strategies, ultimately reducing the burden of cardiovascular disease.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".