Leak compensation in positive pressure ventilators: a lung model study
Bibliographic record
Abstract
Leak compensating abilities of six different positive pressure ventilators commonly used to deliver noninvasive positive pressure ventilation, including the bilevel positive airway pressure (BiPAP) S/T-D and Quantum (Respironics Inc, Murrysville, PA, USA), 335 and O'NYX (Mallinckrodt Inc, St Louis, MO, USA), PLV 102 (Respironics), and Siemens Servo 900C (Siemens Inc, Danvers, MA, USA). Using a test lung model, compensatory capabilities of the ventilators were tested for smaller and larger leaks using the assist/control or timed modes. Back-up rate was 20 min(-1), inspiratory pressure was 18 cmH2O, and expiratory pressure was 5 cmH2O. It was found that even in the absence of air leaking, delivered tidal volume differed substantially between the ventilators during use of pressure-targeted modes, depending on inspiratory flows, inaccuracies in set versus delivered pressures, and inspiratory duration. Also during pressure-targeted ventilation, increasing the tI/ttot up to, but not beyond, 0.5 improved compensation by lengthening inspiratory duration, whereas use of a sensitive flow trigger setting tended to cause autocycling during leaking, interfering with compensation. Leaking interfered with cycling of the BiPAP S/T, inverting the I:E ratio, shortening expiratory time, and reducing delivered tidal volume. Volume-targeted modes achieved limited compensation for small air leaks, but compensated poorly for large leaks. To conclude, leak-compensating capabilities differ markedly between ventilators but pressure-targeted ventilators are preferred for noninvasive positive pressure ventilation in patients with substantial air leaking. Adequate inspiratory flows and durations should be used, triggering sensitivity should be adjusted to prevent autocycling, and a mechanism should be available to limit inspiratory time and avoid I:E ratio inversion.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".