Coronary Artery Disease Severity has a Strong Association with Epicardial Adipose Tissue Thickness
Bibliographic record
Abstract
Objective: Measure the EAT thickness by echocardiography in CAD patients and to compare it with healthy adults of Karachi population. Study Design and setting: It is a case control study. The study was carried out in the radiology (Echocardiography) Department of Ziauddin University Hospital Clifton Campus, Karachi. Methodology: In this study, there were315 participants among them there were159 cases of coronary artery disease and 156 healthy adults. The participants were selected through non probability convenience sampling technique. After filling the proforma including basic information the echocardiography for EAT thickness calculation was done. The data was analyzed on SPSS version 20. Numerical variables were used to derive means and standard deviations while ANOVA was applied to determine the worth along with coronary artery disease and healthy groups. P- Value less than 0.05 was considered as significant. Results: EAT mean thickness was found to be 15.45 ± 7.16 mm. Coronary artery disease group had drastically elevated EAT 16.77 ±9.80 mm in contrast to healthy adults group 14.13 ± 4.52 mm (p=0.02). Three vessel disease group had highest EAT thickness value of 18.79mm while one vessel disease group had lowest Epicardial Adipose Tissue value of 13.55mm. A considerable variation (p=0.001) in EAT thickness and CAD severity was found. Conclusion: Coronary artery diseased CAD group had greater EAT thickness as compared to healthy individuals. Among the CAD patients, the EAT was highest in three vessel disease and was lower in one vessel disease. Keywords: Adipose tissue, Coronary artery disease, Echocardiography, Epicardial
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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 | 0.000 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".