Epicardial Adipose Tissue As New Cardio-Metabolic Risk Marker and Potential Therapeutic Target in the Metabolic Syndrome
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Increased visceral adiposity, is an emerging cardiovascular risk factor. There is now a compelling need to quantify visceral adipose tissue not only for diagnostic purposes, but also for therapeutic interventions with weight reduction drugs or pharmaceuticals targeted to adipose tissue, as well as anti-obesity medications, thiazolidinediones, fibrates, angiotensin receptor blockers, highly active antiretroviral therapy and hormone replacement therapy. Among visceral adipose tissues, growing evidences suggest that cardiac adiposity may play an important role in the development of an unfavorable cardiovascular risk profile. Recent papers suggest that epicardial fat, index of cardiac and visceral adiposity, could locally modulate the morphology and function of the heart. The close anatomical relationship between epicardial adipose tissue and the adjacent myocardium should readily allow local paracrine interactions between these tissues. Echocardiography has been recently proposed for the direct assessment of epicardial adipose tissue. Echocardiographic assessment of epicardial fat may be a helpful tool not only for diagnostic purposes, as marker of visceral adiposity and inflammation, but also for therapeutic interventions with drugs that can modulate the adipose tissue. In this article, epicardial adipose tissue's structure, function, method of assessment and reliability as a diagnostic tool and potential therapeutic target is reviewed.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it