Measurement of resting energy expenditure and its accuracy in women with breast cancer
Bibliographic record
Abstract
BACKGROUND & AIMS: Breast cancer (BC) is frequently linked with obesity, metabolic syndrome, and sarcopenia. Therefore, measuring or accurately estimating resting energy expenditure (REE) is crucial for tailoring nutritional needs, managing weight and prevent under- or over-nutrition. We aimed to measure and compare REE between women with BC and a matched control group. Moreover, the prediction accuracy of selected formulas was evaluated. METHODS: were included in this cross-sectional analysis. Anthropometry, indirect calorimetry, and bioelectrical impedance analysis (BIA) were performed. Patients with BC data were compared to healthy women with similar age and BMI range. Measured REE (mREE) was compared against 15 predictive equations. Agreement between methods was evaluated using Bland-Altman analysis. RESULTS: ) and 75 women as control group. There were no differences in age, anthropometry, and BIA variables between groups, except for percentage fat mass. Measured REE values, alone and adjusted for fat-free mass (FFM) and age, were higher in patients with BC compared to controls (+4.3 % and +6.1 %, respectively). Regarding REE prediction, most of the selected equations underestimated mREE. Precision varied widely, with the two Marra equations showing the highest agreement (73 % and 74.5 %) along with the Müller equation (74 %), however, the wide limit of agreement range indicates substantial variability. CONCLUSIONS: Women with early-stage BC exhibited higher mREE compared to controls, albeit its clinical significance is unknown. None of the selected predictive equations provided accurate and precise REE estimates in this group. Although the Marra equation displayed the highest agreement, further studies are needed to evaluate REE variability and its prediction in women with BC.
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How this classification was reachedexpand
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".