Measurement error of cardiac output determined by nitrous oxide rebreathing and impedance cardiography in healthy adults
Bibliographic record
Abstract
Cardiac output (Q̇) is a fundamental physiological variable but remains challenging to measure. Exercise Q̇ is commonly measured by inert gas rebreathing using acetylene or nitrous oxide (Q̇ N 2 O-IGR ) and impedance cardiography (Q̇ IC ), but device measurement error has not been assessed at different workloads or in females. This study determined the precision of Q̇ N 2 O-IGR (Innocor, COSMED Inc., USA) and Q̇ IC (Physioflow Enduro, Manatec Biomedical, France) in duplicate in 60 adults (30 females; 22 ± 5 years; V̇O 2max : 41.2 ± 8.6 mL . kg −1. min −1 ) during upright rest and cycling at 50 W and 90% peak power output (PPO) (277 ± 71 W). Measurement variance was higher for Q̇ N 2 O-IGR vs. Q̇ IC ( p < 0.0001), at 90% PPO compared to 50 W at rest ( p < 0.001), and in males vs. females at 50 W with Q̇ N 2 O-IGR ( p = 0.005). At rest the typical error (TE), coefficient of variation (CV), and intraclass correlation (ICC) were 0.6 L/min (12.4%), 11.6 ± 8.5%, and 0.75 [0.62–0.82] for Q̇ N 2 O-IGR and 0.4 L/min (6.9%), 7.5 ± 8.4, and 0.87 [0.79–0.92] for Q̇ IC . At 50 W, the TE, CV, and ICC were 0.8 L/min (7.7%), 5.4 ± 5.8%, and 0.79 [0.68–0.87] for Q̇ N 2 O-IGR and 0.5 L/min (5.6%), 3.7 ± 3.8%, and 0.85 [0.76–0.91] for Q̇ IC . At 90% PPO, TE, CV, and ICC were 1.2 L/min (7.5%), 5.4 ± 4.3%, and 0.89 [0.82–0.93] for Q̇ N 2 O-IGR and 0.7 L/min (4.0%), 5.0 ± 3.2%, 0.96 [0.93–0.98] for Q̇ IC . Duplicate Q̇ N 2 O-IGR differed up to ∼4 L/min (38%) and Q̇ IC ∼3 L/min (24%). In conclusion, group-level measurement precision was generally better for Q̇ IC than Q̇ N 2 O-IGR but depended on workload and sex. Duplicate Q̇ N 2 O-IGR and Q̇ IC differed substantially; therefore, repeat measures are important.
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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 | 0.004 | 0.012 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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 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".