Leg fat-free mass and inter-limb leg fat-free mass asymmetry comparisons between bioelectrical impedance analysis and dual-energy X-ray absorptiometry in male career firefighters
Bibliographic record
Abstract
The purpose of this study was to compare dominant and non-dominant leg fat-free mass (FFM) estimations and inter-limb leg FFM asymmetry detection between multi-frequency bioelectrical impedance analysis (MF-BIA) and dual-energy X-ray absorptiometry (DXA) in career firefighters. Sixty-one male career firefighters (age = 31.9 ± 7.4 years; stature = 179.0 ± 7.6 cm; body mass = 89.9 ± 17.4 kg) volunteered for the investigation and reported to the laboratory on one occasion. Leg FFM was estimated by measuring the conduction impedance measured through the corresponding sensing and injecting electrodes for MF-BIA and by outlining both legs into regions of interest for DXA. Inter-limb leg FFM asymmetry was calculated by subtracting the non-dominant limb FFM from the dominant limb FFM, dividing it by the dominant limb FFM, and expressed as a percentage. Asymmetry was defined as having inter-limb leg FFM asymmetry of ±3%. Paired sample t tests were used to examine differences in dominant and non-dominant limb FFM estimates and the McNemar's test was performed to compare inter-device frequencies of asymmetry detection. An alpha level of 0.05 was utilized a priori to determine statistical significance. Results indicated that MF-BIA significantly underestimated FFM for both the dominant (mean difference = 2.00 kg; P < 0.001) and non-dominant (mean difference = 1.97 kg; P < 0.001) limbs compared to DXA. Additionally, MF-BIA detected significantly ( P = 0.004) fewer cases (two) of inter-limb leg FFM asymmetry when compared to DXA (12-cases). Although MF-BIA may be a practical field assessment to track whole-body and segmental-body compositional changes over time, it may not be as sensitive as DXA to identify inter-limb leg asymmetries in career firefighters.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".