Accuracy of two Generic Prediction Equations and One Population-Specific Equation for Resting Energy Expenditure in Individuals with Spinal Cord Injury
Bibliographic record
Abstract
PURPOSE: The primary aim was to assess the accuracy of common prediction equations, the Harris-Benedict (HB) and the Mifflin St. Jeor (MSJ) equations, for estimating resting energy expenditure (REE) among people with spinal cord injury (SCI) against actual REE measurements. The secondary aim was to cross-validate the Buchholz et al. energy prediction equation created for people with SCI. METHODS: A metabolic cart with canopy was used to measure the actual REE. The HB, MSJ, and the Buchholz et al. equations were used for the prediction of REE. RESULTS: Thirty-nine participants (31 males and 8 females) were enrolled in this cross-sectional study. The REEs significantly differed from one another, F(1.52, 57.68) = 52.04, P < 0.001, where both the HB (M = 1703.06, SD = 265.1) and the MSJ (M = 1628.92, SD = 233.8) energy predictions were significantly higher (P < 0.001) than the measured REE (M = 1394.05, SD = 298.7). In contrast, the Buchholz et al. equation did not differ from the measured REE. CONCLUSIONS: Our data show that the HB and MSJ equations do not accurately predict the energy needs of this community. Using a SCI-specific equation would improve estimates of REE, such as the Buchholz et al. equation. More research into energy equations for this population may help health care professionals better tailor dietary requirements for weight management.
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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 | 0.008 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| 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".