Subject–ventilator synchrony during neural versus pneumatically triggered non-invasive helmet ventilation
Bibliographic record
Abstract
OBJECTIVE: Patient-ventilator synchrony during non-invasive pressure support ventilation with the helmet device is often compromised when conventional pneumatic triggering and cycling-off were used. A possible solution to this shortcoming is to replace the pneumatic triggering with neural triggering and cycling-off-using the diaphragm electrical activity (EA(di)). This signal is insensitive to leaks and to the compliance of the ventilator circuit. DESIGN: Randomized, single-blinded, experimental study. SETTING: University Hospital. PARTICIPANTS AND SUBJECTS: Seven healthy human volunteers. INTERVENTIONS: Pneumatic triggering and cycling-off were compared to neural triggering and cycling-off during NIV delivered with the helmet. MEASUREMENTS AND RESULTS: Triggering and cycling-off delays, wasted efforts, and breathing comfort were determined during restricted breathing efforts (<20% of voluntary maximum EA(di)) with various combinations of pressure support (PSV) (5, 10, 20 cm H(2)O) and respiratory rates (10, 20, 30 breath/min). During pneumatic triggering and cycling-off, the subject-ventilator synchrony was progressively more impaired with increasing respiratory rate and levels of PSV (p < 0.001). During neural triggering and cycling-off, effect of increasing respiratory rate and levels of PSV on subject-ventilator synchrony was minimal. Breathing comfort was higher during neural triggering than during pneumatic triggering (p < 0.001). CONCLUSIONS: The present study demonstrates in healthy subjects that subject-ventilator synchrony, trigger effort, and breathing comfort with a helmet interface are considerably less impaired during increasing levels of PSV and respiratory rates with neural triggering and cycling-off, compared to conventional pneumatic triggering and cycling-off.
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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 | 0.001 | 0.002 |
| 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".