Validation of hand‐held bioelectrical impedance analysis with magnetic resonance imaging for the assessment of body composition in overweight women
Bibliographic record
Abstract
Methods of assessing body composition suitable for use in clinical trials should be accurate, reliable, and easy to perform. One such technique routinely implemented is hand-held bioelectrical impedance analysis (BIA). The validity of this method, however, in body composition assessment of overweight women is not known. The aim of this study was to validate the hand-held BIA technique with magnetic resonance imaging (MRI) for the assessment of body composition in overweight women. Fat mass, percent fat mass, fat-free mass, and percent fat-free mass values estimated by hand-held BIA were compared to those measured by MRI. Thirty-one Caucasian women (50.1 +/- 8.2 years, body mass index of 26.9 +/- 3.1 kg/m(2)) participated in the study. BIA measurements were highly reproducible (technical error (TE) was 0.06 +/- 0.07 kg for fat mass and 0.08 +/- 0.11% for percent fat mass), but were significantly different (P < 0.0001) for each body composition parameter when compared to MRI. BIA underestimated fat mass by 2.3 +/- 3.3 kg and percent fat mass by 5.6 +/- 3.9%. Likewise, BIA overestimated fat free mass by 7.4 +/- 2.7 kg and percent fat free mass by 5.6 +/- 3.2%. No relationship between the bias and the mean of the two measurements was noted suggesting that bias is not related to measurement size. Although hand-held BIA gives reproducible findings, the bias noted for all body composition parameters puts into question the validity of this regional impedance device for use in clinical trials in overweight women.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".