Reliability and limits of transport-ventilators to safely ventilate severe patients in special surge situations
Bibliographic record
Abstract
Abstract Background Intensive Care Units (ICU) have sometimes been overwhelmed by the surge of COVID-19 patients. Extending ICU capacity can be limited by the lack of air and oxygen pressure sources available. Transport ventilators requiring only one O 2 source may be used in such places. Objective To evaluate the performances of four transport ventilators and an ICU ventilator in simulated severe respiratory conditions. Materials and methods Two pneumatic transport ventilators, (Oxylog 3000, Draeger; Osiris 3, Air Liquide Medical Systems), two turbine transport ventilators (Elisee 350, ResMed; Monnal T60, Air Liquide Medical Systems) and an ICU ventilator (Engström Carestation—GE Healthcare) were evaluated on a Michigan test lung. We tested each ventilator with different set volumes (Vt set = 350, 450, 550 ml) and compliances (20 or 50 ml/cmH 2 O) and a resistance of 15 cmH 2 O/l/s based on values described in COVID-19 Acute Respiratory Distress Syndrome. Volume error (percentage of Vt set ) with P 0.1 of 4 cmH 2 O and trigger delay during assist-control ventilation simulating spontaneous breathing activity with P 0.1 of 4 cmH 2 O and 8 cmH 2 O were measured. Results Grouping all conditions, the volume error was 2.9 ± 2.2% for Engström Carestation; 3.6 ± 3.9% for Osiris 3; 2.5 ± 2.1% for Oxylog 3000; 5.4 ± 2.7% for Monnal T60 and 8.8 ± 4.8% for Elisee 350. Grouping all conditions (P 0.1 of 4 cmH 2 O and 8 cmH 2 O), trigger delay was 50 ± 11 ms, 71 ± 8 ms, 132 ± 22 ms, 60 ± 12 and 67 ± 6 ms for Engström Carestation, Osiris 3, Oxylog 3000, Monnal T60 and Elisee 350, respectively. Conclusions In surge situations such as COVID-19 pandemic, transport ventilators may be used to accurately control delivered volumes in locations, where only oxygen pressure supply is available. Performances regarding triggering function are acceptable for three out of the four transport ventilators tested.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".