Bioelectrical Impedance Analysis Overestimates Fat‐Free Mass in Breast Cancer Patients Undergoing Treatment
Bibliographic record
Abstract
Abstract Background Bioelectrical impedance analysis (BIA) is commonly used to assess fat‐free mass (FFM) and fat mass (FM) in breast cancer patients. However, because of the prevalence of overweight, obesity and variable hydration status in these patients, assumptions for existing prediction equations developed in healthy adults may be violated, resulting in inaccurate body composition assessment. Methods We measured whole‐body FFM using single‐frequency BIA (50 kHz) and dual‐energy x‐ray absorptiometry (DXA) in 48 patients undergoing treatment for breast cancer. We applied raw BIA data to 18 previously published FFM prediction equations (FFMBIA) and compared these estimates to DXA (FFMDXA; reference method). Results On average, patients were 52 ± 10 (mean ± SD) years of age and overweight (body mass index: 27.5 ± 5.5 kg/m2; body fat by DXA: 40.1% ± 6.6%). Relative to DXA, BIA overestimated FFM by 4.1 ± 3.4 kg (FFMDXA: 42.0 ± 5.9 kg; FFMBIA: 46.1 ± 3.4 kg). Individual equation‐generated predictions of FFMBIA ranged from 39.6 ± 6.7 to 52.2 ± 5.6 kg, with 16 equations overestimating and 2 equations underestimating FFMBIA compared with FFMDXA. Based on equivalence testing, no equation‐generated estimates were equivalent to DXA. Conclusion Compared with DXA, BIA overestimated FFM in breast cancer patients during treatment. Although several equations performed better than others, none produced values that aligned closely with DXA. Caution should be used when interpreting BIA measurements in this clinical population, and future studies should develop prediction equations specific to breast cancer patients.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".