Validation of a new mixing chamber system for breath-by-breath indirect calorimetry
Bibliographic record
Abstract
Limited validation research exists for applications of breath-by-breath systems of expired gas analysis indirect calorimetry (EGAIC) during exercise. We developed improved hardware and software for breath-by-breath indirect calorimetry (NEW) and validated this system as well as a commercial system (COM) against 2 methods: (i) mechanical ventilation with known calibration gas, and (ii) human subjects testing for 5 min each at rest and cycle ergometer exercise at 100 and 175 W. Mechanical calibration consisted of medical grade and certified calibration gas ((4.95% CO(2), 12.01% O(2), balance N(2)), room air (20.95% O(2), 0.03% CO(2), balance N(2)), and 100% nitrogen), and an air flow turbine calibrated with a 3-L calibration syringe. Ventilation was mimicked manually using complete 3-L calibration syringe manouvers at a rate of 10·min(-1) from a Douglas bag reservoir of calibration gas. The testing of human subjects was completed in a counterbalanced sequence based on 5 repeated tests of all conditions for a single subject. Rest periods of 5 and 10 min followed the 100 and 175 W conditions, respectively. COM and NEW had similar accuracy when tested with known ventilation and gas fractions. However, during human subjects testing COM significantly under-measured carbon dioxide gas fractions, over-measured oxygen gas fractions and minute ventilation, and resulted in errors to each of oxygen uptake, carbon dioxide output, and respiratory exchange ratio. These discrepant findings reveal that controlled ventilation and gas fractions are insufficient to validate breath-by-breath, and perhaps even time-averaged, systems of EGAIC. The errors of the COM system reveal the need for concern over the validity of commercial systems of EGAIC.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".