Comparison of the standard predictive equation for calculation of resting energy expenditure with indirect calorimetry in hospitalized and healthy dogs
Bibliographic record
Abstract
OBJECTIVE: To determine the level of clinical agreement between 2 methods for the measurement of resting energy expenditure (REE). DESIGN: Prospective case series. ANIMALS: 77 dogs. PROCEDURE: Oxygen consumption (Vo2) and CO2 production (Vco2) were measured with an open-flow indirect calorimeter in healthy (n = 10) and ill (67) dogs. Measurements were collected at 3 time periods on 2 days. The Vo2 and the Vco2 measurements were then used to calculate the REE values. RESULTS: Mean values of measured (MREE) and predicted (PREE) REEs in healthy dogs and a dog with medical illnesses or trauma were not significantly different. There was a significant difference on day 2 between the MREE and PREE in the group of dogs recovering from major surgery. More importantly, there was significant variation between the PREE and MREE on an individual-dog basis. The PREE only agreed to within +/- 20% of the MREE in 51% to 57% of the dogs. CONCLUSIONS AND CLINICAL RELEVANCE: The level of agreement between these two methods for determining the 24-hour REE was poor in individual dogs. The level of disagreement between the 2 methods indicates that these methods may not be used interchangeably in a clinical setting. Measurement of REE by use of indirect calorimetry may be the only reliable method of determining REE in an individual ill or healthy dog.
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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 | 0.011 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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".