Bioelectrical Impedance and Dual-Energy X-Ray Absorptiometry Assessments of Changes in Body Composition Following Exercise in Patients with Type 2 Diabetes Mellitus
Bibliographic record
Abstract
We aimed to compare the level of agreement between leg-to-leg bioelectrical impedance analysis (LBIA) and dual-energy X-ray absorptiometry (DXA) for assessing changes in body composition following exercise intervention among individuals with Type 2 diabetes mellitus (T2DM). Forty-four adults with T2DM, age 53.2 ± 9.1 years; BMI 30.8 ± 5.9 kg/m(2) participated in a 6-month exercise program with pre and post intervention assessments of body composition. Fat free mass (FFM), % body fat (%FM) and fat mass (FM) were measured by LBIA (TBF-300A) and DXA. LBIA assessments of changes in %FM and FM post intervention showed good relative agreements with DXA variables (P < 0.001). However, Bland-Altman plot(s) indicated that there were systematic errors in the assessment of the changes in body composition using LBIA compared to DXA such that, the greater the changes in participant body composition, the greater the disparity in body composition data obtained via LBIA versus DXA data (FFM, P = 0.013; %FM, P < 0.001; FM, P < 0.001). In conclusion, assessment of pre and post intervention body composition implies that LBIA is a good tool for assessment qualitative change in body composition (gain or loss) among people with T2DM but is not sufficiently sensitive to track quantitative changes in an individual's body composition.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".