Intra‐Abdominal Adipose Tissue Quantification by Alternative Versus Reference Methods: A Systematic Review and Meta‐Analysis
Bibliographic record
Abstract
Objective This meta‐analysis aimed to assess the agreement between intra‐abdominal adipose tissue (IAAT) quantified by alternative methods and the reference standards, computed tomography (CT) and magnetic resonance imaging (MRI). Methods MEDLINE and EMBASE electronic databases were systematically searched to identify studies that quantified IAAT thickness, area, or volume by a comparator method and CT or MRI. Using an inverse variance weighted approach (random‐effects model), the mean differences and 95% limits of agreement (LoA) were pooled between methods. Results The meta‐analysis included 24 studies using four comparator methods. The pooled mean differences were −0.3 cm (95% LoA: −3.4 to 3.2 cm; P = 0.400) for ultrasound and −11.6 cm2 (95% LoA: −43.1 to 19.9 cm2; P = 0.004) for bioelectrical impedance analysis. Dual‐energy x‐ray absorptiometry (DXA) quantified both IAAT area and volume with mean differences of 8.1 cm2 (95% LoA: −98.9 to 115.1 cm2; P = 0.061) and 10 cm3 (95% LoA: −280 to 300 cm3; P = 0.808), respectively. Conclusions Ultrasound and DXA measure IAAT with minimal bias from CT or MRI, while bioelectrical impedance analysis systematically underestimates IAAT. However, with the exception of DXA for IAAT volume, the wide LoA caution against clinical or research use of the comparator methods and emphasize the need to optimize alternatives to the reference standards.
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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 | 0.027 | 0.062 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.020 | 0.038 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".