Accuracy of the Common Predictive Equations for Estimating Resting Energy Expenditure among Normal and Overweight Girl University Students
Bibliographic record
Abstract
OBJECTIVE: The aim of the present study was to determine the accuracy of commonly used predictive formulas of resting metabolic rate (RMR) compared to measured RMR in normal and overweight young females. METHODS: In this cross-sectional study, 98 female university students aged 18-30 years with body mass index 18 to 30 kg/m(2) were recruited. Anthropometric indices and body compositions were measured. RMR was measured by indirect calorimetry (FitMate, Cosmed, Rome, Italy) and estimated by 11 predictive formulas. The accuracy of the RMR formulas and mean percentage differences between estimated and measured values were calculated. Paired t test was used to compare estimated and measured RMRs. RESULT: There were no significant differences between measured and estimated RMR by the 4 commonly used formulas (Mifflin, Cunningham, and World Health Organization [WHO]/Food and Agriculture Organization [FAO]). Among all of the equations, the Mifflin formula showed the lowest bias (-2.97 ± 116.43 kcal/day) at the group level and was the most accurate formula (80.23%) in normal and overweight participants. The over- and underestimated values were about 14% and 5.5%, respectively. In normal and overweight females, Mifflin was the most accurate formula, with 75.51% and 84.61% accuracy, respectively. CONCLUSION: Given the current lack of a standardized formula that consistently delivers accurate results, the Mifflin formula can be recommended for estimating energy requirements in normal and overweight females in clinical practice.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 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".