A generalized method for controlling end-tidal respiratory gases during nonsteady physiological conditions
Bibliographic record
Abstract
While forcing of end-tidal gases by regulating inspired gas concentrations is a common technique for studying cardiorespiratory physiology, independently controlling end-tidal gases is technically challenging. Feedforward control methods are challenging because end-tidal values vary as a dynamic function of both inspired gases and other nonregulated physiological parameters. Conventional feedback control is limited by delays within the lungs and body tissues and within the end-tidal forcing system itself. Consequently, modern end-tidal forcing studies have generally restricted their analysis to simple time courses of end-tidal gases and to resting steady-state conditions. To overcome these limitations, we have designed and validated a more generalized end-tidal forcing system that removes the need for manual tuning and rule-of-thumb based control heuristics, while allowing for accurate control of gases along spontaneous and complicated time courses and under nonsteady physiological conditions. On average during resting, steady walking, and walking with time varying speed, our system achieved step changes in PetCO2 within 3.0 ± 0.9 (mean ± SD) breaths and PetO2 within 4.4 ± 0.9 breaths, while also maintaining small steady-state errors of 0.1 ± 0.2 mmHg for PetCO2 and 0.3 ± 0.8 mmHg for PetO2. The system also accurately tracked more complicated changes in end-tidal values through a bandwidth of 1/10 the respiratory (sampling) frequency, a practical limit of feedback control systems. The primary mechanism enabling this controller performance is a generic mathematical model of the cardiopulmonary system that captures the breath-by-breath relationship between inspired and end-tidal gas concentrations, with secondary contributions from reduced delays in controlled air delivery.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".