Application of Stable Isotope Tracers in the Study of Exercise Metabolism in Children: A Primer
Bibliographic record
Abstract
Exercise metabolism in children has traditionally been assessed using the respiratory exchange ratio (RER) to determine the contributions of fat and carbohydrate to the exercise energy demands. Although easily measured, RER measurements have limitations. Other methods to assess metabolism such as the obtainment of a muscle biopsy and the use of nuclear magnetic resonance spectroscopy carry ethical and feasibility concerns, respectively, which limit their use in studies involving children. Stable isotopes, used routinely in studies involving adults, can also be applied in studies involving children in an ethical and feasible manner. Two common stable isotopes used in metabolic studies involving children include carbon-13 (C) and nitrogen-15 (N). C-glucose can be used to study carbohydrate metabolism and N-glycine can be used to assess protein metabolism. This article reviews the use of C-glucose and N-glycine to study exercise metabolism in children, considers some of the associated ethical aspects, explains the general methodology involved in administering these isotopes and the resources required, and describes studies involving children utilizing these methods. Finally, suggestions for future research are provided to encourage further use of these techniques.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".