Design and performance testing of a novel emergency ventilator for in-hospital use
Bibliographic record
Abstract
BACKGROUND: The rapidly evolving COVID-19 pandemic has led to increased use of critical care resources, particularly mechanical ventilators. Amidst growing concerns that the health care system could face a shortage of ventilators in the future, there is a need for an affordable, simple, easy to use, emergency stockpile ventilator. METHODS: Our team of engineers and clinicians designed and tested an emergency ventilator that uses a single limb portable ventilator circuit. The circuit is controlled by a pneumatic signal with electronic microcontroller input, using air and oxygen sources found in standard patient rooms. Ventilator performance was assessed using an IngMar ASL 5000 breathing simulator, and it was compared with a commercially available mechanical ventilator. RESULTS: The emergency ventilator provides volume control mode, intermittent mandatory ventilation and continuous positive airway pressure. It can generate tidal volumes between 300 and 800 mL with <10% error, with pressure, volume, and waveforms substantially equivalent to existing commercial ventilators. CONCLUSIONS: We describe a cost effective, safe, and easy to use ventilator that can be rapidly manufactured to address ventilator shortages in a pandemic setting. It meets basic clinical needs and can be provided for emergency use in cases requiring mechanical ventilation because of complications due to respiratory failure from infectious diseases.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".