Accuracy of Predicting Resting Energy Expenditure in Individuals with Spinal Cord Injury
Bibliographic record
Abstract
Obesity is a health problem that is estimated to affect over 60% of people with spinal cord injury (SCI). After a SCI, energy requirements are lowered, therefore health professionals struggle to help people with SCI plan meals and estimate their caloric needs. Little is known about the accuracy of traditional resting energy expenditure (REE) prediction equations in this population. PURPOSE: To assess the accuracy of the Harris-Benedict (HB) and the Mifflin St. Jeor (MSJ) equations for estimating REE among people with SCI against actual REE measurements. METHODS: A metabolic cart with canopy was used to measure the actual REE. The HB equation and the MSJ equation were used for the prediction of REE. RESULTS: Thirty-five participants (27 males and 8 females) were enrolled in this cross-sectional study. The mean age, height and weight were 43.8±12.4yrs, 173.2±8.7cm, and 78.1±16.8kg, respectively. A repeated-measures ANOVA found that the REEs significantly differed from one another, F(1.1, 37.8)= 51.24, p<.001. The actual REE (M=1354.1, SD=257.0 kcal) was significantly lower (p<.001) than both the HB (M=1679.2, SD=281.6 kcal) and the MSJ (M=1615.4, SD=238.2kcal) energy predictions. CONCLUSION: The prevalence of obesity among people with SCI is alarmingly high, a population-specific prediction equation for REE may be required to help these individuals better manage their weight, given that traditional prediction equations appear to overestimate REE.
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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 | 0.003 | 0.013 |
| 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 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".