New insights into total and resting energy expenditure using state-of-the-art methods in cancer survivors: a cross-sectional study
Bibliographic record
Abstract
BACKGROUND: Precise measurement of energy expenditure is essential for guiding nutritional care after cancer treatment. However, commonly used predictive equations may be inaccurate for individuals recovering from cancer. Leveraging state-of-the-art methods can offer valuable insights into the actual energy requirements of cancer survivors upon treatment completion. OBJECTIVES: The aim of this study was to characterize total (TEE) and resting energy expenditure (REE) and to assess the accuracy of predictive equations against measured values in posttreatment colorectal cancer survivors (CRCSs). METHODS: ), and body composition]. RESULTS: Twenty participants (equal sex distribution; mean ± SD age: 61.4 ± 14.1 y; BMI: 28.8 ± 6.4) were included. Most had a history of colon cancer (55%) and stage III disease (75%). Predictive equations (predicted TEE range: ∼2060-2500 kcal/d; predicted REE range: ∼1230-1730 kcal/d) commonly underestimated measured TEE (∼2460 ± 680 kcal/d) (n = 2, 50%) and REE (∼1700 ± 330 kcal/d) (n = 19, 86.4%). Dietary Reference Intake equations with estimated physical activity level had the highest individual-level accuracy for TEE prediction but still resulted in substantial intra-individual variability (∼≤1400 kcal error). BMI and body composition were positively related to percentage bias in TEE but not REE equations. For REE, the Johnstone and Harris-Benedict equations showed the best individual-level agreement but still exhibited high intraindividual variability, with errors up to ∼≤460 kcal and ∼≤530 kcal, respectively. CONCLUSIONS: The majority of CRCSs exhibit higher energy expenditure than estimated by standard prediction equations, underscoring the need to validate these equations in populations with cancer to optimize accuracy. Improved methods for assessing energy expenditure are needed to guide long-term survivorship care.
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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 | 0.004 | 0.005 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".