Reliability and limits of transport-ventilators to safely ventilate severe patients in special surge situations
Bibliographic record
Abstract
Abstract Background Several Intensive Care Units (ICU) have been overwhelmed by the surge of COVID-19 patients thus necessitating to extend ventilation capacity outside the ICU where air and oxygen pressure are not always available. Transport ventilators requiring only O 2 source may be used to deliver volume-controlled ventilation. Objective To evaluate the performances of four transport ventilators compared to an ICU ventilator simulating severe respiratory conditions. Materials and methods Two pneumatic transport ventilators, (Oxylog 3000, Draeger; Osiris 3, Air Liquide Medical Systems) and two turbine transport ventilators (Elisee 350, ResMed; Monnal T60, Air Liquide Medical Systems) were compared to an ICU ventilator (Engström Carestation – GE Healthcare) using a Michigan training test lung. We tested each ventilator with different set volumes Vt set (350, 450, 550 ml) and different compliances (20 or 50 ml/cmH 2 O) and a resistance of 15 cmH 2 0/L/sec based on values recently described in COVID-19 Acute Respiratory Distress Syndrome. Volume error was measured, as well as the trigger time delay during assist-control ventilation simulating spontaneous breathing activity with a P 0.1 of 4 cmH 2 0. 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, trigger delay was 42 ± 4 ms, 65 ± 5 ms, 151 ± 14 ms, 51 ± 6 and 64 ± 5 ms for Engström Carestation, Osiris 3, Oxylog 3000, Monnal T60 and Elisee 350, respectively. Conclusions In special surge situations such as COVID-19 pandemic, most transport ventilators may be used to safely deliver volume-controlled ventilation in locations where only oxygen pressure supply is available with acceptable volume accuracy. Performances regarding triggering function are generally acceptable but vary across ventilators.
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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 | 0.003 | 0.021 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".