Characteristics of Rapidly Manufactured Ventilators: A Scoping Review
Bibliographic record
Abstract
Many health care systems worldwide were ill-prepared for the mass-casualty surge caused by the COVID-19 pandemic. Mechanical ventilator shortages prompted the production of rapidly manufactured ventilators (RMVs). However, without standards to develop them, the effectiveness and safety of RMVs remain uncertain. The purpose of this study was to map the breadth and depth of the literature on RMVs and provide suggestions for effective and safe designs. A scoping review, following the Joanna Briggs Institute guidelines, was completed. A search of 9 electronic databases and Google Scholar was completed in April 2022 and updated in 2024. Dual screening and data extraction were conducted using predefined criteria based on 6 previously published RMV guidance documents. Results were collated into descriptive summaries and tables and used to develop the suggested standards. There were 66 RMVs described within 66 articles. The majority (60, 91%) of articles were published post-COVID-19 (2020), with 24 (36%) from the United States. Four designs were identified: 18 (27%) electro-pneumatic (E-P), 27 (41%) automatic compression of manual resuscitator (MR), 6 (9%) automatic compression of MR with E-P components (E-P and MR), and 15 (23%) "other." The E-P designs mimicked conventional ventilators and MR designs incorporated an MR with a motor and arm. Four RMV characteristic categories emerged from the data: operating features, performance features, other features outside routine use, and engineering features. There was significant variability in the RMV designs. Eleven suggestions regarding RMV design, performance, and testing were developed. This study provides preliminary information to inform the standardization of RMV designs to guide future manufacturing for effective and safe use. Although pandemic urgency has waned, RMV utility may extend to future mass-casualty scenarios (eg, natural disasters, wars) and in low- and middle-income countries, which often lack sufficient resources even under normal conditions.
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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 | 0.022 | 0.113 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.007 |
| Bibliometrics | 0.037 | 0.031 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".