Validity of dual‐energy x‐ray absorptiometry for estimation of visceral adipose tissue and visceral adipose tissue change after surgery‐induced weight loss in women with severe obesity
Bibliographic record
Abstract
OBJECTIVE: Reliable and simple methods to quantify visceral adipose tissue (VAT) and VAT changes are needed. This study investigated the validity of dual-energy x-ray absorptiometry (DXA) compared with magnetic resonance imaging (MRI) for estimating VAT cross sectionally and longitudinally after surgery-induced weight loss in women with severe obesity. METHODS: Women with obesity (n = 36; mean age 43 [SD 10] years; 89% White) with DXA and MRI before bariatric surgery (T0) at 12 (T12) and 24 months (T24) post surgery were included. CoreScan (GE Healthcare, Chicago, Illinois) estimated VAT from 20% of the distance between the top of the iliac crest and the base of the skull. MRI VAT (total VAT) was measured from the base of the heart to the sacrum/coccyx on a whole-body scan. RESULTS: Mean DXA VAT was 45% of MRI VAT at T0, 46% at T12, and 68% at T24. DXA underestimated change in MRI VAT between T0 and T12 by 26.1% (0.81 kg, p = 0.03) and by 71.7% (0.43 kg, p < 0.001) between T12 and T24. The relationship between DXA VAT and MRI VAT differed between T12 and T24 (p value for interaction = 0.03). CONCLUSIONS: CoreScan lacks validity for comparing VAT across individuals or for estimating the size of changes within individuals; however, within the limits of measurement error, it may provide a useful indicator of whether some VAT change has occurred within an individual.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".