Association of pericardial adipose tissue volume with presence and severity of coronary atherosclerosis
Bibliographic record
Abstract
PURPOSE: This study was to investigate whether high pericardial adipose tissue (PAT) volume is related to the presence and severity of coronary artery disease (CAD). METHODS: Consecutive patients (310 patients) who underwent both dual-source 64-slice CT and percutaneous coronary angiography were recruited into this study. Waist circumference (WC), body mass index (BMI), blood biochemical variables, coronary artery calcium (CAC) score and Gensini score were measured. Pericardial adipose tissue (PAT) volume was determined by dual-source CT. RESULTS: PAT volume was positively correlated with BMI, WC, gender (male), hypertension, diabetes, age, total cholesterol and low-density lipoprotein-cholesterol. PAT volume in CAD patients was significantly higher than that in patients without CAD (238.36 ± 81.21 cm3 vs. 200.13±72.34 cm3). PAT volumes in patients with multi-vessel lesions were significantly higher than those with one-vessel lesions (P < 0.001). A significant correlation between PAT volume and CAC score (r=0.305, P < 0.001) was found. PAT volume was an independent factor affecting Gensini score. CONCLUSION: PAT volume was significantly correlated with traditional cardiovascular risk factors, the severity of coronary atherosclerosis and the number of stenotic coronary vessels. Thus, PAT volume may be a reliable marker to evaluate the presence and severity of CAD.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.004 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".