Closed-Loop Control of Respiratory Drive Using Pressure-Support Ventilation
Bibliographic record
Abstract
By using diaphragm electrical activity (multiple-array esophageal electrode) as an index of respiratory drive, and allowing such activity above or below a preset target range to indicate an increased or reduced demand for ventilatory assistance (target drive ventilation), we evaluated whether the level of pressure-support ventilation can be automatically adjusted in response to exercise-induced changes in ventilatory demand. Eleven healthy individuals breathed through a circuit (18 cm H2O/L/second inspiratory resistance at 1 L/second flow; 0.5-1.0 L/second expiratory flow limitation) connected to a modified ventilator. Subjects breathed for 6-minute periods at rest and during 20 and 40 W of bicycle exercise, with and without target drive ventilation (the target was set to 60% of the increase in diaphragm electrical activity observed between rest and 20 W of unassisted exercise). With target drive ventilation during exercise, the level of pressure-support ventilation was automatically increased, reaching 13.3 +/- 4.0 and 20.3 +/- 2.8 cm H2O during 20- and 40-W exercise, respectively, whereas diaphragm electrical activity was reduced to a level within the target range. Both diaphragmatic pressure-time product and end-tidal CO2 were significantly reduced with target drive ventilation at the end of the 20- (p < 0.01) and 40-W (p < 0.001) exercise periods. Minute ventilation was not altered. These results demonstrate that target drive ventilation can automatically adjust pressure-support ventilation, maintaining a constant neural drive and compensating for changes in respiratory demand.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".