Accuracy and Applicability of Resting Metabolic Rate Prediction Equations Differ for Women Across the Lifespan
Bibliographic record
Abstract
Background: Nutrition clinicians need accurate and reliable resting metabolic rate (RMR) assessments to determine energy needs and an appropriate nutrition care plan. Material/Methods: This cross-sectional study compared the accuracy of eight RMR prediction equations (Harris-Benedict, Robertson and Reid, Cunningham 1980, FAO/WHO/UNU, Owen, Mifflin-St. Jeor, Cunningham 1991, and Nelson) to measured RMR by indirect calorimetry among young (n=57; age: 25±3 years), midlife (n=57; age: 44±3 years), and older (n=46; age: 68±5 years) women. Paired t-tests examined differences between predicted and measured RMR. Statistical analyses were conducted using SPSS (version 21), with significance defined as p<0.05. Bland-Altman plots displayed prediction bias and agreement. Prediction accuracy was defined when predicted RMR was ±10% of measured RMR. Serum thyroid stimulating hormone and follicle stimulating hormone concentrations were measured to assess thyroid function and ovarian reserve, respectively. Results: The difference between predicted and measured RMR ranged from +0.6% (Owen) to +17.7% (Cunningham 1980) for the young, -2.8% (Nelson) to +18.1% (Cunningham 1980) midlife, and +2.8 (Nelson) to +26.7% (Cunningham 1980) older women. For the young women, only the Owen equation predicted RMR similar to measured RMR (p=0.905). For the older women, only the Nelson equation predicted RMR similar to measured RMR (p=0.051). All estimates using prediction equations were significantly different from measured values for midlife women. Conclusion: Many RMR prediction equations have limited applicability for women at difference stages of the lifespan, thus impacting patient outcomes. Additional research is necessary to determine the appropriateness of RMR prediction equations among women of all ages.
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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 | 0.011 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".