Bioelectrical Impedance Analysis Overestimates Fat‐Free Mass in Breast Cancer Patients Undergoing Treatment
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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 (FFM BIA ) and compared these estimates to DXA (FFM DXA ; reference method). Results On average, patients were 52 ± 10 (mean ± SD) years of age and overweight (body mass index: 27.5 ± 5.5 kg/m 2 ; body fat by DXA: 40.1% ± 6.6%). Relative to DXA, BIA overestimated FFM by 4.1 ± 3.4 kg (FFM DXA : 42.0 ± 5.9 kg; FFM BIA : 46.1 ± 3.4 kg). Individual equation‐generated predictions of FFM BIA ranged from 39.6 ± 6.7 to 52.2 ± 5.6 kg, with 16 equations overestimating and 2 equations underestimating FFM BIA compared with FFM DXA . 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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it