Accuracy of a Portable Indirect Calorimeter for Measuring Resting Energy Expenditure in Individuals With Cancer
Bibliographic record
Abstract
BACKGROUND: Determining optimal caloric intake for an individual with cancer is complicated by metabolic changes that occur, namely, alterations in resting energy expenditure (REE). There is currently no validated clinically available equation or tool to measure energy expenditure in these patients. METHODS: Patients with newly diagnosed solid tumors underwent REE assessments using the FitMate GS portable indirect calorimeter and reference VMax metabolic cart; both used canopy hoods. REE was also estimated from the Harris-Benedict, Mifflin St. Jeor, and Henry equations for comparison. Data were analyzed using paired samples t-test and the Bland-Altman approach to assess group-level and individual-level agreement compared with the metabolic cart. RESULTS: ; age: 62 ± 10 years) participated in the study. Biases for the FitMate GS and both equations were low (ranging from -44 to -92 kcal or -2.3% to -5.1%), indicating good group-level accuracy. The FitMate GS had low bias, but the widest limits of agreement (-28.0% to 21.2%) compared with the 3 equations (Harris-Benedict: -15.8% to 11.2%; Mifflin St. Jeor: -17.1% to 6.9%; Henry: -15.4% to 11.5%). These differences were not due to volume of oxygen, BMI category, or sex. CONCLUSION: FitMate GS performed well on a group level, but its accuracy was poor on an individual level. Further research should develop better equations and validate tools to measure energy expenditure for accurate dietary recommendations for patients at nutrition risk.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".