Modeling Oxygen Delivery to Spontaneously Breathing Individuals: Improving Oxygenation Using a Feedback Controller
Bibliographic record
Abstract
This study employed a simulation approach to model oxygen delivery in spontaneously breathing patients with chronic obstructive pulmonary disease (COPD). The model of respiratory and circulatory systems developed by Morozoff et al., originally designed for mechanically ventilated patients with a fixed fraction of inspired oxygen (FIO2), was adapted to incorporate the relationship between oxygen flow delivered through nasal cannula and FIO2, along with COPD-specific pathophysiological parameters. The effectiveness of constant and variable oxygen flow delivery was evaluated using a closed-loop control system with proportional (P) and proportional-integral-derivative (PID) controllers. The adapted model of respiratory and circulatory systems successfully reproduced SpO2 variations observed in COPD patients, capturing desaturation patterns during rapid eye movement sleep and daily activities. Simulations showed that continuous oxygen flow was inadequate for maintaining SpO2 within the target range. P and PID controllers improved SpO2 regulation, increasing time within the target range (88%-92%) to 80% and 74%, respectively, compared to a maximum of 55% achieved with a constant oxygen flow system. However, as airway resistance increased compared to the baseline case, the P-controller's performance declined considerably compared to PID-controller, highlighting the need for retuning controller to have the highest possible efficiency considering varying pathophysiological parameters. In addition, more advanced control strategies, such as model-based controllers, may enhance adaptability to dynamic patient conditions. These findings support the development of adaptive oxygen delivery strategies, optimizing treatment outcomes for spontaneously breathing COPD patients requiring long-term oxygen therapy.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".