Quantitative Comparison of Ventilation Parameters of Different Approaches to Ventilator Splitting and Multiplexing
Bibliographic record
Abstract
CONTEXT: Amid the COVID-19 pandemic, this study delves into ventilator shortages, exploring simple split ventilation (SSV), simple differential ventilation (SDV), and differential multiventilation (DMV). The knowledge gap centers on understanding their performance and safety implications. HYPOTHESIS: Our hypothesis posits that SSV, SDV, and DMV offer solutions to the ventilator crisis. Rigorous testing was anticipated to unveil advantages and limitations, aiding the development of effective ventilation approaches. METHODS AND MODELS: Using a specialized test bed, SSV, SDV, and DMV were compared. Simulated lungs in a controlled setting facilitated measurements with sensors. Statistical analysis honed in on parameters like peak inspiratory pressure (PIP) and positive end-expiratory pressure. RESULTS: Setting target PIP at 15 cm H2O for lung 1 and 12.5 cm H2O for lung 2, SSV revealed a PIP of 15.67 ± 0.2 cm H2O for both lungs, with tidal volume (Vt) at 152.9 ± 9 mL. In SDV, lung 1 had a PIP of 25.69 ± 0.2 cm H2O, lung 2 at 24.73 ± 0.2 cm H2O, and Vts of 464.3 ± 0.9 mL and 453.1 ± 10 mL, respectively. DMV trials showed lung 1's PIP at 13.97 ± 0.06 cm H2O, lung 2 at 12.30 ± 0.04 cm H2O, with Vts of 125.8 ± 0.004 mL and 104.4 ± 0.003 mL, respectively. INTERPRETATION AND CONCLUSIONS: This study enriches understanding of ventilator sharing strategy, emphasizing the need for careful selection. DMV, offering individualization while maintaining circuit continuity, stands out. Findings lay the foundation for robust multiplexing strategies, enhancing ventilator management in crises.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".