Pericardial Fat Is Associated With Prevalent Atrial Fibrillation
Bibliographic record
Abstract
BACKGROUND: Obesity represents an important risk factor for atrial fibrillation (AF). We tested the hypothesis that pericardial fat, a unique fat deposit in close anatomic proximity to cardiac structures and autonomic fibers, is associated with prevalent AF. METHODS AND RESULTS: Participants from the Framingham Heart Study underwent multidetector computed tomography from 2002 to 2005. We estimated the association between quantitative pericardial, intrathoracic and visceral adipose tissue volumes (per standard deviation of volume) with prevalent AF adjusting for established AF risk factors (age, sex, systolic blood pressure, blood pressure treatment, PR interval, and clinically significant valvular disease). Of the 3217 eligible participants (mean age, 50.6+/-10.1 years; 48% women), 54 had a confirmed diagnosis of AF. Pericardial fat but not intrathoracic or visceral abdominal fat was associated with prevalent AF in multivariable-adjusted models (odds ratio per standard deviation of pericardial fat volume, 1.28; 95% confidence intervals, 1.03 to 1.58). Further adjustments for body mass index, heart failure, myocardial infarction, and intrathoracic fat volume did not materially change the association between pericardial fat and AF. CONCLUSIONS: Pericardial fat was associated with prevalent AF even after adjustment for AF risk factors, including body mass index. If this association is replicated, further investigations into the mechanisms linking pericardial fat to AF are merited.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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".