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 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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".