Racial differences in visceral adipose tissue but not anthropometric markers of health-related variables
Bibliographic record
Abstract
This study sought to determine whether visceral adipose tissue (VAT) and/or its anthropometric surrogates could significantly predict health-related variables (HRV) in overweight Caucasian (CC) (n = 36) and African-American (AA) (n = 30) women. With the use of magnetic resonance imaging, findings showed significantly higher volume and area of VAT (P < 0.0001 for both) as well as higher triacylglycerol (P = 0.009) in CC compared with AA women. Furthermore, VAT volume, race, and VAT volume x race interaction could significantly predict triacylglycerol (P = 0.0094), high-density lipoprotein cholesterol (P = 0.0057), insulin (P = 0.0002), and insulin resistance (P < 0. 0001). Additionally, the VAT volume x race interaction for insulin (P = 0.040) and insulin resistance (P = 0.003) was significant. In a separate analysis, waist circumference and race predicted the identical variables. Our results support the use of volume or area of VAT in predicting HRV in CC women; however, its use in AA women appears limited. In contrast, waist circumference can provide a suitable VAT alternative for both CC and AA women; however, VAT clearly represents the more powerful predictor.
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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 | 0.000 | 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".