Threshold Values of High‐risk Echocardiographic Epicardial Fat Thickness
Bibliographic record
Abstract
OBJECTIVE: Echocardiographic epicardial adipose tissue is a new index of cardiac and visceral adiposity with great potential as a diagnostic tool and therapeutic target. In this study, we sought to provide threshold values of echocardiographic epicardial fat thickness associated with metabolic and anthropometric risk factors. METHODS AND PROCEDURES: Epicardial fat thickness was measured in 246 consecutive white subjects (120 women, 126 men, median age 46 years (30-65), median BMI 32 kg/m(2) (22-52), median waist circumference 100.5 cm (85-140)), who underwent routine transthoracic echocardiogram for standard clinical indications. Metabolic syndrome (MetS), Insulin resistance, BMI, and waist circumference categories were identified and epicardial fat was calculated. RESULTS: Among 246 subjects, 58% had MetS. These subjects showed median values of epicardial fat thickness of 9.5 and 7.5 mm (in men and women, respectively), significantly higher than those found in subjects without MetS (no MetS) (P < 0.001). Receiver operating characteristics (ROC) analysis showed that epicardial fat thickness of 9.5 and 7.5 mm maximize the sensitivity and specificity to predict MetS, in men and women, respectively. In separate analyses, median epicardial fat thickness values of 9.5 and 7.5 mm were cutoff points associated with high abdominal fat in men and women, respectively. When insulin sensitivity was considered separately, epicardial fat thickness of 9.5 mm was associated with insulin resistance. DISCUSSION: Median values of 9.5 and 7.5 mm should be considered the threshold values for high-risk echocardiographic epicardial fat thickness in white men and women, respectively. Echocardiographic epicardial fat measurement may be of help for cardiometabolic risk stratification and therapeutic interventions targeting the fat.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".