Comparison of resting metabolic rate prediction equations in college-aged adults
Bibliographic record
Abstract
Prediction equations have been considered an accurate method for estimating resting metabolic rate (RMR) across multiple populations, but their accuracy for college-aged individuals not on an athletics team remains to be determined. Sixty-two college-aged (18–30 yrs) males (n = 31) and females (n = 31) had their RMR measured (RMRm), using indirect calorimetry, and body composition assessed via air-displacement plethysmography. The World Health Organization (WHO), Mifflin−St Jeor (Mifflin), Harris−Benedict (HB), Cunningham, and Nelson equations were used to estimate RMR. No difference was observed between the Cunningham and RMRm regardless of sex (p ≥ 0.05). All other prediction equations estimated a significantly lower RMR for males (p < 0.05). The Mifflin and Nelson equations predicted an RMR that was significantly lower than RMRm for females (p < 0.05). When compared with RMRm, no difference was detected for females using the WHO, HB, or Cunningham (p ≥ 0.05). Only the Nelson equation predicted an RMR that was outside of the clinically acceptable range (±10% of RMRm) regardless of sex. The Cunningham, WHO, and HB equations can accurately predict RMR for college-aged males and females. RMR prediction equations used in this study are less accurate for those with greater RMRs. Novelty: For adults 18–30 years old that are not on an athletics team, the Cunningham equation can accurately predict RMR. The Nelson equation should not be used to predict RMR for this population. There is a systematic bias for RMR prediction equations to underestimate higher measured RMR values.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".