Using an Allometric Equation to Accurately Predict the Energy Expenditure of Children and Adolescents With Nonalcoholic Fatty Liver Disease
Bibliographic record
Abstract
BACKGROUND: Pediatric patients with nonalcoholic fatty liver disease (NAFLD) require targeted nutrition therapy that relies on calculating energy needs. Common energy equations are inaccurate in predicting resting energy expenditure (REE), influencing total energy expenditure (TEE) estimates. Equations based on allometric scaling are simple, accurate, void of subjective activity and/or stress factor bias, and they estimate TEE. OBJECTIVE: To investigate the predictive accuracy of an allometric energy equation (AEE) in predicting TEE of children and adolescents with NAFLD. METHODS: Retrospective study performed in a single institution. The allometric equation was used to calculate AEE, and the results were compared with TEE calculated using indirect calorimetry data (measured REE) multiplied by an activity factor (AF) of 1.5 or 1.7. RESULTS: Fifty-six patients with a mean age of 13 years were included in this study. The agreement between TEE (using an AF of 1.5) and AEE was -96 kcal/d (confidence interval, -29 to 221). The predictive accuracy of the allometric equation was not different between obese and nonobese patients. CONCLUSIONS: Allometric equations allow for accurate estimation of TEE in children with NAFLD.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".