Predicted and Measured Resting Metabolic Rate: In Young, Non-obese Women
Bibliographic record
Abstract
PURPOSE: Measured resting metabolic rate (RMR) was compared with predicted RMR in a sample of young, non-obese women. METHODS: In 52 women aged 19 to 30 with a body mass index of 16 to 29 kg/m2, RMR was measured with a MedGem indirect calorimeter and predicted with five commonly used equations: the Harris-Benedict (1919), Mifflin (1989), Owen (1985), Schofield (weight) (1985), and Schofield (weight and height) (1985) equations. Measured RMR and predicted RMR were compared through the use of various measures. RESULTS: In comparison with the measured RMR, the RMR predicted with four of the five equations was significantly higher (by 16 to 225 kcal/day, p < 0.001). At the group level, the Owen equation performed best and captured the greatest proportion of individuals (65%) for whom predicted RMR differed from measured RMR by less than 10%. With the other four equations, residuals exceeded 10% for more than two-thirds of participants. For the Harris-Benedict, Mifflin, and Owen equations, every 100 kcal/day increase in measured RMR was associated with a 6% to 8% decrease in error. The optimal prediction range (within 10% of the measured RMR) was different for each: Owen equation 1105 to 1400 kcal/day, Mifflin equation 1280 to 1595 kcal/day, and Harris-Benedict equation 1345 to 1630 kcal/day. CONCLUSIONS: Prediction equations should be modified according to the amount of corresponding percentage error. Where possible, RMR should be measured. Barring this, the Owen equation should be used for young, non-obese women.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".