How to interpret and apply the results of indirect calorimetry studies: A case-based tutorial
Bibliographic record
Abstract
Evidence is growing that the individual adjustment of energy targets guided by indirect calorimetry (IC) can improve outcome. With the development of a new generation of devices that are easier to use and rapid, it appears important to share knowledge and expertise that may be used to individualize nutrition care. Despite the focus of this tutorial being on one contemporary device, the principles of IC apply across existing devices and can assist tailoring the nutrition prescription and in assessing response to nutrition therapy. The present tutorial addresses its clinical application in intubated mechanically ventilated and spontaneously breathing adult patients (canopy), i.e. it covers the range from critical illness to outpatients. The cases that are presented show how the measured energy expenditure (mEE), and the respiratory quotient (RQ), i.e. the ratio of expired CO 2 to consumed O 2 , should be applied in different cases, to adapt and individualize nutrition prescription, as it is a good marker of over- or underfeeding at the different stages of disease. The RQ also informs about the patient's body's capacity to use different substrates: the variations of RQ indicating the metabolic changes revealing insufficient or excessive feeding. The different cases reflect the use of a new generation device as a metabolic monitor that should be combined with other clinical observations and laboratory biomarkers. The tutorial also points to some shortcomings of the method, proposing alternatives.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".