Development and assessment of the performance of a shared ventilatory system that uses clinically available components to individualize tidal volumes
Bibliographic record
Abstract
Abstract Objectives To develop and assess a system for shared ventilation using clinically available components to individualize tidal volumes. Design Evaluation and in vitro validation study Setting Ventilator shortage during the SARS-CoV-2 pandemic. Participants The team consisted of physicians, bioengineers, computer programmers, and medical technology professionals. Methods Using clinically available components, a system of ventilation consisting of two ventilatory limbs was assembled and connected to a ventilator. Monitors for each limb were developed using open-source software. Firstly, the effect of altering ventilator settings on tidal volumes delivered to each limb was determined. Secondly, the impact of altering the compliance and resistance of one limb on the tidal volumes delivered to both limbs was analysed. Experiments were repeated three times to determine system variability. Results The system permitted accurate and reproducible titration of tidal volumes to each limb over a range of ventilator settings and simulated lung conditions. Alteration of ventilator inspiratory pressures, of respiratory rates, and I:E ratio resulted in very similar tidal volumes delivered to each limb. Alteration of compliance and resistance in one limb resulted in reproducible alterations in tidal volume to that test lung, with little change to tidal volumes in the other lung. All tidal volumes delivered were reproducible. Conclusions We demonstrate the reliability of a shared ventilation system assembled using commonly available clinical components that allows titration of individual tidal volumes. This system may be useful as a strategy of last resort for Covid-19, or other mass casualty situations, where the need for ventilators exceeds supply.
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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 | 0.004 | 0.008 |
| 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 | 0.002 |
| 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".