P- 52 CORRELATION AMONG DIFFERENT METHODS TO ESTIMATE BODY AND LIVER FAT IN HEALTHY ADULTS
Bibliographic record
Abstract
An increase in body fat is a risk factor to develop fatty liver disease. Until now, few studies have related different methods to estimate body fat related to liver fat. We aimed to determinate the correlation among different methods to estimate body and liver fat in healthy adults. In a cross- sectional study registered in ethics and research committees GAS3794 evaluating healthy adults; chronic illness, uncontrolled diabetes, hypertension or thyroid, bariatric surgery or pacemaker, were not included. The estimation of body fat (BF) was by 3 methods, two with bioelectric impedance of 19 frequencies (19F), 4 frequencies (4F) and the third by the Durnin- Womersley (DW) skinfold thickness formulae; also, were measured body mass index (BMI), waist circumference (WC) and visceral fat (VF). The liver fat was estimated by controlled attenuation parameter (CAP) using transitory elastography. Correlations were calculated with Pearson coefficient among body composition methods and CAP; each anthropometric isolated parameter was associated with hepatic steatosis grades by a logistic regression analysis using SPSS v21.0. In 231 participants, mean age was 41.8 years (SD 11.3), WC 91 cm (SD 12), BMI 27.8 kg/m2 (SD 4.6). 112 had some grade of steatosis (S3 n=72, S2 n=18). The correlation among 3 methods was on average r= 0.853 (p=0.000), and between CAP and BF was 0.290 (p= 0.000). BMI, WC, VF and suprailiac skinfold thickness showed correlations of r=0.570 (p=0.000), r=0.477 (p=0.000), r=0.393 (p= 0.000) y r= 0.471 (p=0.000) respectively. Regression analysis demonstrated that BMI (OR=1.32, p=0.000), WC > 80 women and > 90 cm men (OR=14.7, p=0.010), VF (OR=1.8, p=0.008) and suprailiac skinfold thickness (OR=1.14, p=0.000) showed association with steatosis. Three methods were like to estimate body fat although they were not able to represent the liver fat; waist circumference was the best indicator related with steatosis
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".