Flow meter, PEEP valve development and performance testing for use in a volume-controlled emergency use ventilator (EUV-SK1)
Bibliographic record
Abstract
In March 2020 Health Canada (HC) released its “Interim order respecting the importation and sale of medical devices for use in relation to COVID-19”[1]. This led to the development, testing and HC approval of an emergency use ventilator (EUV-SK1) by a Saskatoon-based engineering and manufacturing company (RMD Engineering), with support from members of the Saskatchewan Health Authority (SHA) and subject matter experts from several colleges at the University of Saskatchewan (USask). The EUV-SK1 was designed as a volume-controlled ventilator, using medical air and oxygen. The main considerations for the design were minimizing the number of moving parts, keeping the majority of manufacturing inhouse and using readily available materials. These were important risk mitigation strategies amidst disruptions to the global supply chain and business closures during lockdown. At the start of the project, it became rapidly apparent that two of the critical components, the flow meter and the PEEP (positive end expiratory pressure) valve would have to be developed and built inhouse. The current industry standard for flow measurement in ventilators are mass flow meters, however, they were not available at the time, and all PEEP devices were of a proprietary nature, and again, unobtainable. Boyles law and the Venturi effect were leaned on, and differential pressure across an inhouse design of a variable rate mechanical orifice was used to overcome precision flow challenges. The PEEP, being the most critical component, required development of a proportionally controlled solenoid on top of a balanced check valve, both developed and produced inhouse during the pandemic. Performance testing was completed using a commercial test lung (active servo lung, ASL 5000TM, Ingmar Medical). Testing was performed using the settings provided by Table 201.104 in ISO 80601-2-80 [2]. The EUV-SK 1 was further subjected to extensive inhouse and third-party testing for reliability and safety.
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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 | 0.009 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.005 |
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".