Validity of electrical impedance myography to estimate percent body fat: comparison to bio-electrical impedance and dual-energy X-ray absorptiometry
Bibliographic record
Abstract
BACKGROUND: Assessment of percent body fat (%BF) is typically measured with bioelectrical impedance (BIA) as a proxy for dual-energy X-ray absorptiometry (DXA). Notably, poorer agreement between BIA and DXA among persons who are overweight or obese has been reported. The use of electrical impedance myography (EIM) as a proxy for DXA has not been validated. The objective was to evaluate an EIM device and two multi-frequency BIA devices with the reference standard (DXA) stratified by weight status and gender. METHODS: In a convenience sample of 82 adults, %BF assessed by EIM and two BIA devices was compared to DXA. Agreement between devices was tested with intra-class correlation coefficients (ICC) and Bland-Altman plots. RESULTS: Agreement between DXA and EIM (ICC=0.77) was poorer than the agreement between either BIA device with DXA (ICC>0.87). Stratified by sex, agreement between EIM and DXA was greater for men than women (ICC=0.81 and ICC=0.61, respectively). Stratified by BMI, agreement between EIM and DXA was best for normal-weight individuals (ICC=0.89) and progressively poorer for overweight (ICC=0.80) and obese (ICC=0.67) individuals. Bland-Altman plots revealed wide limits of agreement and an increase in EIM mean difference as average %BF increased. Similar trends were seen in BIA assessments. CONCLUSIONS: EIM and BIA substantially underestimate %BF in overweight and obese individuals. Wide limits of agreement coupled with variable ICC limit device interchangeability with one another and limit clinical utility.
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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 | 0.018 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".